Corpus-Based Studies of Translational Chinese in English- Chinese Translation New Frontiers in Translation Studies

New Frontiers in Translation Studies

Richard Xiao Xianyao Hu Corpus-Based Studies of Translational Chinese in English- Chinese Translation New Frontiers in Translation Studies

Series editor Defeng Li Centre for Translation Studies, SOAS, University of London, London, United Kingdom Centre for Studies of Translation, Interpreting and Cognition, University of Macau, Macau SAR More information about this series at http://www.springer.com/series/11894 Richard Xiao • Xianyao Hu

Corpus-Based Studies of Translational Chinese in English-Chinese Translation Richard Xiao Xianyao Hu Linguistics and English Language College of International Studies Lancaster University Southwest University Lancaster , United Kingdom Chongqing , China

ISSN 2197-8689 ISSN 2197-8697 (electronic) New Frontiers in Translation Studies ISBN 978-3-642-41362-9 ISBN 978-3-642-41363-6 (eBook) DOI 10.1007/978-3-642-41363-6

Library of Congress Control Number: 2015942914

Springer Heidelberg New York Dordrecht London © Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 This work is subject to copyright. All rights are reserved by the Publishers, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made.

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The Chinese texts in this book, when they appear for the ﬁ rst time in a paragraph, are presented ﬁ rst in Chinese characters, which are followed by pinyin transcription, a literal translation, and a normal, idiomatic translation whenever necessary.

General Edit or’s Preface

New Frontiers in Translation Studies, as its name suggests, is a Series which focuses on new and emerging themes in Translation Studies. The last four decades have witnessed a rapid growth of this ﬂ edgling discipline. This Series intends to publish and promote these developments and provide readers with theories and methods they need to carry out their own translation studies projects. Translation Studies is now expanding into new or underexplored areas both in theories and research methods. One recent development is the keen interest in translation theories that transcend Eurocentrism. Translation Studies has for decades been dominated by Western modes of understanding and theorizing about translation and closed to models of other traditions. This is due to, as many have argued, the “unavailability of reliable data and systematic analysis of translation activities in non-European cultures” (Hung and Wakabayashi 2005). So in the past few years, some scholars have attempted to make available literature on translation from non- European traditions (Cheung 2006). Several conferences have been held with themes devoted to Asian translation traditions. Besides, rather than developing translation theories via a shift to focusing on non-Eurocentric approaches, efforts have been directed towards investigating translation universals applicable across all languages, cultures and traditions. Modern Translation Studies has adopted an interdisciplinary approach from its inception. Besides tapping into theories and concepts of neighbouring disciplines, such as linguistics, anthropology, education, sociology, and literary studies, it has also borrowed research models and methods from other disciplines. In the late 1970s, German translation scholars applied Think-aloud Protocols (TAPs) of cognitive psychology in their investigation of translators’ mental processes, and more recently, process researchers have incorporated into their research designs lab methods, such as eye-tracker, EEG and fMRI. In the early 1990s, computational and corpus linguistics was introduced into Translation Studies, which has since gener- ated a proliferation of studies on the so-called translation universals, translator style, and features of translated language. Studies on interpreting and translation education have also taken a data-based empirical approach and yielded interesting and useful results.

vii viii General Editor’s Preface

As Translation Studies seeks further growth as an independent discipline and recognition from outside the translation studies community, the interest to explore beyond the Eurocentric translation traditions will continue to grow. So does the need to adopt more data- and lab-based methods in the investigations of translation and interpreting. It is therefore the intent of this Series to capture the newest developments in these areas and promote research along these lines. The monographs or edited volumes in this Series will be selected either because of their focus on non- European translation traditions or their application of innovative research methods and models, or both. We hope that translation teachers and researchers, as well as graduate students, will use these books in order to get acquainted with new ideas and frontiers in Translation Studies, carry out their own innovative projects and even contribute to the Series with their pioneering research.

Defeng Li

References

Cheung, M. 2006. An anthology of Chinese discourse on translation, volume one: From earliest times to the Buddhist project . Manchester/Kinderhook: St. Jerome Publishing. Hung, E. and J. Wakabayashi. 2005. Asian translation traditions . Manchester/Northampton: St Jerome. Acknowledgments

This book presents the major outputs of our corpus-based translation studies over the past years, which have been supported by China’s National Planning Ofﬁ ce for Philosophy and Social Sciences (NPOPSS grant reference 07BYY011), Ministry of Education under its Program for New Century Excellent Talents in University (grant reference NCET-11-0460), as well as the UK’s Economic and Social Research Council (ESRC grant reference ES/K010107/1), to which we are greatly indebted. We are grateful to Rebecca Zhu, editor at Springer, for her unfailing support and great patience while we worked on this book, without which the book would not have been possible. We also thank Professor Wang Kefei and Professor Hu Kaibao, the editors in chief of the original series of corpus-based research in Chinese, and Mr. Guan Xinchao at Shanghai Jiaotong University Press for the support they have provided in publishing the Chinese edition of the book on which the present volume is based.

March, 2015 Richard Xiao and Xianyao Hu

Contents

1 Introduction ...... 1 1.1 Paradigmatic Shifts in Translation Studies ...... 2 1.2 The Objectives and Signifi cance of the Research ...... 4 1.3 An Overview of the Book ...... 6 2 Corpus-Based Translation Studies: An Evolving Paradigm ...... 9 2.1 The Corpus “Revolution” in Linguistic Research ...... 9 2.2 Corpora Used in Contrastive Linguistic Research and Translation Studies ...... 11 2.2.1 Monolingual Versus Multilingual Corpora ...... 11 2.2.2 Parallel Versus Comparable Corpora ...... 11 2.2.3 Comparable Versus Comparative Corpora ...... 12 2.2.4 General Versus Specialised Corpora ...... 13 2.3 Corpus-Based Translation Studies: The State of the Art ...... 14 2.3.1 Applied Translation Studies ...... 14 2.3.2 Descriptive Translation Studies ...... 16 2.3.3 Theoretical Translation Studies ...... 20 3 Exploring the Features of Translational Language ...... 21 3.1 The Translation Universals Hypotheses ...... 22 3.1.1 Explicitation ...... 22 3.1.2 Simplifi cation ...... 23 3.1.3 Normalisation ...... 24 3.1.4 Other Translation Universals Hypotheses ...... 24 3.2 The State of the Art of Corpus-Based Translation Studies in Chinese ...... 25 3.3 Specifi c Research in the Linguistic Features of Translational Chinese ...... 27 3.3.1 The Lexical Features of Translational Chinese ...... 27 3.3.2 The Syntactical Features of Translational Chinese ...... 31 3.4 Problems in the Current Research ...... 34

xi xii Contents

4 Corpora and Corpus Tools in Use ...... 37 4.1 Current General Corpora of Chinese in Use ...... 38 4.2 The Lancaster Corpus of Mandarin Chinese...... 40 4.2.1 The Brown Corpus or LOB Model ...... 40 4.2.2 The Sampling Frame and Text Collection ...... 41 4.2.3 Encoding and Markup ...... 44 4.2.4 Segmentation and POS Annotation ...... 47 4.3 The Zhejiang University Corpus of Translational Chinese ...... 48 4.3.1 Corpus Design ...... 49 4.3.2 Encoding and Markup ...... 50 4.3.3 Segmentation and POS Annotation ...... 51 4.3.4 The Upgraded Version of LCMC ...... 52 4.4 Parallel Corpora Used in This Research ...... 53 4.4.1 The Babel English-Chinese Parallel Corpus ...... 53 4.4.2 The General Chinese-English Parallel Corpus ...... 54 4.5 Corpus Analytical and Statistical Tools ...... 54 4.5.1 Xaira ...... 54 4.5.2 WordSmith Tools ...... 58 4.5.3 ParaConc ...... 65 5 The Macro-Statistic Features of Translational Chinese ...... 67 5.1 Lexical Density and Textual Information Load ...... 67 5.2 Analyses of Word Frequencies and Mean Word Length ...... 71 5.3 Mean Sentence and Paragraph Length ...... 75 5.4 Word Clusters ...... 78 6 The Lexical Features of Translational Chinese ...... 89 6.1 Keywords and Key Word Classes in LCMC and ZCTC ...... 89 6.2 Word Classes and Word-Class Clusters in LCMC and ZCTC ...... 93 6.3 Distribution of Punctuation Marks in LCMC and ZCTC ...... 99 6.4 Pronouns in LCMC and ZCTC ...... 101 6.5 Connectives in LCMC and ZCTC ...... 104 6.6 Idioms in LCMC and ZCTC ...... 106 6.7 Reformulation Markers in LCMC and ZCTC ...... 110 7 The Grammatical Features of Translational Chinese ...... 121 7.1 Bei Passive Constructions in LCMC and ZCTC ...... 121 7.2 Ba Disposal Constructions in LCMC and ZCTC ...... 131 7.3 Existential You Sentences in LCMC and ZCTC ...... 135 7.4 Shi Sentences in LCMC and ZCTC ...... 138 7.5 Suo Sentences LCMC and ZCTC ...... 142 7.6 Lian Constructions in LCMC and ZCTC...... 144 7.7 Classiﬁ ers in LCMC and ZCTC ...... 146 7.8 Aspect Markers in LCMC and ZCTC ...... 149 7.9 Structural Auxiliaries in LCMC and ZCTC ...... 152 7.10 Modal Particles in LCMC and ZCTC ...... 154 Contents xiii

8 The Features of Translational Chinese and Translation Universals ..... 157 8.1 The Macro-Statistic Features of Translational Chinese ...... 157 8.2 The Lexical Features of Translational Chinese ...... 160 8.3 The Grammatical Features of Translational Chinese ...... 163 9 Conclusive Remarks ...... 169 9.1 Translation Universals Hypotheses Re-evaluated from the Chinese Perspective ...... 169 9.2 Contributions of the Research Presented in the Book...... 173 9.3 Directions for Future Research ...... 174

Appendices ...... 177 Appendix 1: Tagset of LCMC and ZCTC ...... 177 Appendix 2: Keywords in ZCTC ...... 180 Appendix 3: Notes for the Corpora and Tools Available with This Book ...... 183

References ...... 185 References in English ...... 185 References in Chinese ...... 194

Index ...... 203

Abbreviations

ASP Aspect marker DUR -zhe , durative aspect marker (⵰) PFV -le , perfective aspect marker (Ҷ) PST -guo , experiential aspect marker (䗷) B A ba, marker for preposed object (ᢺ) B E shi , copula (ᱟ) BNC British National Corpus CL Classifi er ATT/DE -de , structural auxiliary, attributive marker (Ⲵ) ADV -de , structural auxiliary, adverbial marker (ൠ) CMP -de, structural auxiliary, complemental marker (ᗇ) FLOB Freiburg-LOB Corpus of British English INT Passive intensifi er gei (㔉), suo (ᡰ) LCMC Lancaster Corpus of Mandarin Chinese LL Log-likelihood test PL Plural suffi x -men (Ԝ) PRT Particle PSV Syntactic passive marker RVC Resultative verb complement SL source language TL target language TEC Translational English Corpus ZCTC Zhejiang University Corpus of Translational Chinese

Authors’ Bionotes

Richard Xiao is Reader in Corpus Linguistics and Chinese Linguistics (Honorary) in the Department of Linguistics and English Language at Lancaster University in the UK and a professor of linguistics at Zhejiang University in China. His main research inter- ests include corpus linguistics, contrastive and translation studies of English and Chinese, and tense and aspect theory. He has published dozens of journal articles and numerous books including Aspect in Mandarin Chinese (John Benjamins, 2004), Corpus-Based Language Studies (Routledge, 2006), A Frequency Dictionary of Mandarin Chinese (Routledge, 2009), Using Corpora in Contrastive and Translation Studies (Cambridge Scholars, 2010), and Corpus-Based Contrastive Studies of English and Chinese (Routledge, 2010).

Xianyao Hu worked as a research associate in the Department of Linguistics and English Language at Lancaster University in 2014 while holding a professor- ship in linguistics and translation studies in the College of International Studies at Southwest University in China. He had earned his Ph.D. in translation studies from East China Normal University in 2006 and worked as post-doctoral researcher at Beijing Foreign Studies University and as Fulbright visiting scholar at the University of California, Los Angeles.

xvii Chapter 1 Introduction

The objective of this book is to describe systematically the linguistic characteristics of texts translated into Chinese in relation to native writings in the same language with an ultimate interest in the so-called translation universals, which were fi rst defi ned by Baker ( 1993 : 43) as the “universal features of translation, that is features which typically occur in translated text rather than original utterances and which are not the result of interference from specifi c linguistic systems”. This defi nition can be less strongly formulated as the general tendencies, regularities or typical linguistic features which are found in translational language either in terms of their differentiating features from the source language ( SL ) or in terms of their deviations from the target language ( TL ) which is usually thought to be the native tongue of the translator. On the one hand the interference effect of the SL upon the translation is admittedly inevitable; the language of the translated texts, on the other hand, seems to be different to the TL as well (McEnery and Xiao 2007a ). The second type of differences can be detected by contrastive studies between the translated texts and the comparable non-translated, native writings in the same language. The research into this has already shown some potential powers to the investigation of the translating process; to the studies of the translational norms, i.e. the socialcultural norms sanctioning translating activities; as well as to the illuminating idea of the “ Third Code ” proposed by Frawley in 1984. The authors of this book will try to elaborate on those potential powers by under- taking qualitative and quantitative corpus-based research projects that involve contrastive and comparative studies of the translated Chinese and/or English texts in contrast to their respective native writings. We will incorporate both macro and micro observations into the comprehensive analyses of the linguistic features of the translated Chinese from multiple perspectives and at different levels. Apparently, one of the major objectives of these analyses is to explore the typical or discriminat- ing lexical and grammatical features of translated Chinese; the ambition of the research, however, is not limited to the experimental linguistic depiction but stretches to many other factors, either practical or theoretical, for example, the

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 1 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_1 2 1 Introduction extent of the SL interference upon the translation in the TL, which may be discernible through a parallel corpus contrastive observation between the SL and TL texts, and the commonalities and discrepancies between the translated languages, for instance, the translated English and the translated Chinese, which have recently become possible through a higher level of contrastive studies based on comparable corpora of the translated versus the non-translated or native texts in both languages, respectively. The later concern can be considered as one of the efforts to testify the existence of the translation universals between the translations in two genetically distinct major languages in the world, English and Chinese, hence, to establish evidence for the existence of the “ Third Code ”. This introductory chapter will begin with a brief review of the paradigmatic shifts within translation studies throughout the past half century for the sake of off- setting the scene for the research presented in this book. It will then present a clear- cut introduction to the objectives and signiﬁ cance of the current research, followed by an overview of the structure of the book.

1.1 Paradigmatic Shifts in Translation Studies

Translation studies can be defi ned as a scholarly discipline that is concerned with “the complex of problems clustered round the phenomenon of translating and translations” (Holmes 1987 : 9). According to the Holmes-Toury map (cf. Munday 2001 ), translation studies can be theoretical , descriptive or applied. Since 1990s, the translation studies based on or driven by corpus linguistic data and tools have made signifi cant progresses in the research, having brought great vitality to the discipline either in terms of the descriptive or the theoretical studies and also shed light on the applied branch of the discipline (Xiao and Dai 2010a , 2011 ; Xiao et al. 2010). One of the advantages of the corpus-based approach is that it can reveal the “regularities of actual behaviour” (Toury 1995 : 265). The establishment of corpus- based translation studies ( CTS ) as a new paradigm was preceded by two paradigmatic shifts in theoretical translation studies. The fi rst shift is from “prescriptive” to “descriptive”. Prescriptive translation studies were dominant before the mid-1950s, for example, Alexander Tytler’s (1747–1814) three principles of translation; Friedrich Schleiermacher’s (1768–1834) foreignising translation; Yan Fu’s (1854– (xin (fi delity), 䗮 da (fl uency) and 䳵 ya (elegance); Nida’s (1964 : 166 ؑ (1921 dynamic equivalence; and Newmark’s (1981 ) distinction between semantic versus communicative translation. The 1950s saw a paradigmatic shift in translation studies from prescriptive to descriptive, which is represented by descriptive translation studies (Toury 1995 ). The second shift is from the micro (i.e. linguistic) to macro (i.e. sociocultural) perspective. The period between the mid-1950s and the mid-1980s was what Fawcett (1997 ) calls the “heroic age” of linguistically oriented translation studies, which focused on word, phrase and sentence levels. The socioculturally oriented translation studies have become the mainstream since the mid-1980s, which integrate with literary and linguistic theories such as feminism, postcolonialism and 1.1 Paradigmatic Shifts in Translation Studies 3 discourse and ideology. In the 1980s, the traditional illusion of an “ideal” translation gradually gave way to a more realistic view that translating is a communicative act in a sociocultural context and that translations are an integral part of the receiving culture (cf. Bassnett-McGuire 1991 ; Bassnett and Lefevere 1990 ). It is clear even from this brief review that different approaches have been taken to translation studies, from the earlier workshop approach, the philosophical and linguistic approach and the functionalist approach to descriptive translation studies, the post-structuralist and postmodernist approaches and the cultural studies approach. Nevertheless, there has been a gap between translation theory and practice, and practice is lagging far behind theory. On the one hand, the same translation phenomenon can be explained by many different competing theories so that these theories lack their uniqueness. On the other hand, the majority of phenomena in translation cannot be explained by existing translation theories. Under the infl uence of New Firthian scholars such as Halliday and Sinclair, target- system-oriented translation projects were carried out in Australia and the UK (Bell 1991 ; Baker 1992 ). Meanwhile, the descriptive methodology continued to gain supporters among comparative literature scholars and polysystem theorists (cf. Tymoczko 1998 : 652). Toury ( 1995 : 1) argues that “no empirical science can make a claim for completeness and (relative) autonomy unless it has a proper descriptive branch”. Arguments like this have had a great impact on descriptive translation studies, which has shifted the focus in translation research from the relationship between source and target texts to translations per se. With the rapid development of corpus linguistics in the mid-1980s, corpus linguists started to be interested in translated texts, initially literary texts such as novels. For example, Gellerstam (1986 ) studied translated English from Swedish, casting new light on what has been known as “ translationese ”. Bell ( 1991 : 39) proposed to observe translator performance by analysing the translation product through “fi nding features in the data of the product which suggest the existence of particular elements and systematic relations in the process”. His proposal sparked great interest in building and exploring corpora of translated texts, with the aim of analysing features of translational language for evidence of the relationship between translation as a product and translation as a process. Corpora are useful in this respect because they help to reveal “relations between frequency and typicality, and instance and norm” (Stubbs 2001a : 151). According to Baker (1993 : 243), “[t]he most important task that awaits the application of corpus techniques in translation studies […] is the elucidation of the nature of translated text as a mediated communicative event”. As we will see in this book, corpora and corpus linguistic techniques provide a powerful tool to identify the characteristic features of translational language (i.e. the so-called translation universals), which provide evidence for the translation process per se or, in Baker’s words, to “understand what translation is and how it works”. Laviosa (1998a ) observes that “the corpus-based approach is evolving, through theoretical elaboration and empirical realisation, into a coherent, composite and rich paradigm that addresses a variety of issues pertaining to theory, description, and the practice of translation”. Three factors have collaboratively contributed to the convergence between corpus research and translation studies, in our view. They are (1) the 4 1 Introduction hypotheses that translation universals can be tested by corpus data; (2) the rapid development of corpus linguistics, especially of multilingual corpus research in the early 1990s; and fi nally (3) the increasing interest in descriptive translation studies (DTS). The marriage between DTS and corpora is only natural in that corpus linguistics, as a discipline stemming from the description of real linguistic performance, supplies DTS with a systematic method and trustworthy data. Tymoczko (1998 ) predicated more than two decades ago that “Corpus Translation Studies is central to the way that Translation Studies as a discipline will remain vital and move forward”. This predication has been realised by an ever-growing number of corpus- based translation studies, for example, van Leuven-Zwart and Ton Naaijkens (1991); Venuti (1995); Kenny ( 2001 ); Bowker (2002); Laviosa ( 2002 ); Granger et al. ( 2003 ); Bosseaux (2004); Hansen et al. (2004); Mauranen and Kujamäki (2004); Olohan (2004 ); Santos (2004); Zanettin et al. (2004); Anderman and Rogers (2007); Johansson (2007); Gilquin et al. (2008); Barlow (2009); Beeby et al. (2009); Saldanha (2009); Hruzov (2010); Izwaini (2010); Tengku Mahadi et al. (2010); Veronis (2010); and Kruger et al. (2011 ). Studies such as these have led to a better understanding of the scope, signifi cance, usefulness and appropriateness of corpora in studying the processes, products as well as functions of translation. It is also worthwhile to note that a series of international conferences on the theoretical and methodological issues of corpus-based translation studies, for example, the biennial conference of Using Corpora in Contrastive and Translation Studies (UCCTS), have been held in Europe and other places of the world (cf. Xiao 2009c ; Dai and Xiao 2011a ). Corpus-based translation studies has increasingly been seen not only as a legitimate part of contrastive language study but also as a crucial way of revealing inherent features of translational language which as a special variant, like any other varieties of a natural language, deserves to be investigated in its own right (cf. Mauranen 2002: 165). In general, corpus-based translation studies, which has been rapidly developing not only in Europe but also in Asia and South America, among other regions, has established itself as a vital subdiscipline of the current translation studies. Many of the theoretical and methodological issues of CTS have been profoundly discussed and settled down by a wide range of translation theorists across the world. In Asia, for example, Wang (2011 ) and Hu (2011 ) have, respectively, addressed the theoretical foundation, key concepts, the framework of research and the existing problems of the subdiscipline in great details. The present volume is one of the attempts to further systemise the new paradigm of translation studies, with particular interest in probing into the linguistic features of Chinese translations from English within the framework of translation universals .

1.2 The Objectives and Signiﬁ cance of the Research

The majority of previous studies on translation universals have so far been carried out and testiﬁ ed in the English texts translated from and/or into other European languages which are genetically close to one another (e.g. Mauranen and Kujamäki 1.2 The Objectives and Signiﬁ cance of the Research 5

2004 ; see Chap. 3 for a comprehensive review of this). The only large-sized corpus of translational language in existence, i.e. the Translational English Corpus ( TEC ) built by Mona Baker and her colleagues at the University of Manchester in the 1990s, which has been widely used by researchers in the pursuit of translation universals , has limited these studies largely within the European languages. Nevertheless, previous studies based on these limited language pairs have supplied us with evidence of the distinct linguistic features of translational English (and a few other European languages) which are different from the source language as well as from the native target language . There have also been some similar studies based on a few other European languages other than English as the target language of translating, but by and large they still fall within the small group of closely related languages. Due to this limitation of linguistic affi nity, the researchers are still uncer- tain whether the theoretical models or hypotheses of translation universals are at least partly applicable to the translation between distinctly different languages such as English and Chinese. Can we fi nd similar tendencies or typical features in the translation from English to Chinese or vice versa? Needless to say, if any of the TUs hypotheses are to be held as universals , the research of the second type, i.e. the research of the translation between languages which are not genetically related to each other, is not only necessary but also theoretically and methodologically more convincing. The current book is an attempt of this effort to overcome the bottleneck of linguistic affi nity in the previous studies of translation universals . Qualitative and quantitative analyses are to be carried out on the translated texts between English and Chinese, two typologically distinct and distant major languages in the world. As we will see in Chap. 3, corpus-based studies of translation between English and Chinese, although numerous in number, are actually quite limited in the methodologies applied. Except for a few studies based on parallel translation corpus (e.g. Qin and Wang 2004 ; Wang 2005 ; Huang 2007 and Hu 2008b ), the empirical investigations of translational English or Chinese for their own sake based on comparable translational corpus, i.e. the corpus of translated texts only, are very rare or unsys- tematic in their research designs. As can be revealed by China’s National Knowledge Infrastructure (CNKI), one of the most prestigious databases of academic journals and dissertations in China, there were only six journal articles published in 2006 that were devoted to the research of the linguistic features of translational language among the 262 articles under the keywords of “corpus-based” and “translation” (namely, Hu 2004 , 2005 ; Xie 2004 ; Huang 2006 ; Li 2006a ; Wu and Huang 2006 ). More disappointingly, the six journal articles are all introductory reviews of the corpus-based translation studies carried out in the western countries. Since 2007, there have emerged a number of empirical studies on the linguistic properties of translational Chinese; however, the majority of these projects, monographs and/or journal articles still focus their attention on the translated texts of a particular genre, for example, translated fi ction or the translation of science and technology (see Chap. 3 for details). Few of these studies have taken into consideration the linguistic and stylistic variation across registers or genres. Some of the studies on linguistic variation across registers (e.g. Biber 1988 , 1995; Xiao 2009a ) have shown that different registers tend to have signifi cantly different linguistic and stylistic prefer- 6 1 Introduction ences (particularly in terms of the quantifi able scale). If we continue to neglect these register variation issues, the generalisation of universal tendencies, be it translational or non-translational, will not be possible. Consequently, the current book will use as its empirical foundation a balanced corpus of translational Chinese, which in turn will not only provide a more convincing empirical basis in the present study but also become a new landmark for descriptive translation studies and the studies of translation universals in particular. It is also worthy of pointing out that, methodologically, the corpus-based translation studies that has been carried on since the 1990s seems to favour the monolingual contrastive analytical methodology, i.e. the contrastive studies of native writings and the translated texts in the same TL (exemplifi ed by the research of Baker, Kenny and Laviosa on translation universals in the past two decades). Certainly there have been more researchers who follow the more traditional track of bilingual contrastive studies of the SL and TL texts (e.g. Xiao and McEnery 2005a ). Quite aware of the limitations of both approaches, we have decided to take a composite paradigm of research proposed by McEnery and Xiao ( 2002 ), which combines the two corpus methods, parallel and comparable, by offering additional perspectives of observing translational language and the possibilities of including a wider range of linguistic features. As a quick summary of this section, we would like to reiterate that this book is devoted to the quantitative and systematic research of the linguistic properties of translational Chinese based on both balanced comparable and parallel corpora . It is supplementary to the existing descriptive translation studies and the studies of translation universals in the sense of fi lling in the gap of research between two genetically distinct and distant major languages in the world, Chinese and English. Meanwhile, the combination of contrastive linguistic analysis and translation studies will prove to be effective and fruitful. And hopefully, the composite methodology of both comparable and parallel corpus approaches will be a breakthrough in the paradigmatic shifts of translation studies. Finally, the fi ndings of linguistic exploration of translational Chinese will be a new contribution to corpus-based descriptive translation studies.

1.3 An Overview of the Book

As noted in the previous sections, this book is an attempt of corpus-based qualitative and quantitative studies of the typical linguistic and textual features of the translated Chinese texts. In the second chapter, we will fi rst discuss the application of corpus linguistic methodologies to the research of language, particularly to the research of translation studies, which will hopefully give the reader an idea of the theoretical foundation of the book in general. Chapter 3 will then supply the reader with an up-to-date comprehensive review of many of the studies on translational language, especially a detailed review of the investigations into translational Chinese. With both the theoretical and empirical preparations, the reader will be 1.3 An Overview of the Book 7 introduced to the corpora and corpus linguistic tools that have been involved and used in Chap. 4 . Chapter 5 is devoted to the macro-level observations of the corpus of translational Chinese in relation to the corpus of non-translational, i.e. native Chinese. The reader can see a comprehensive list of contrastive analyses of the lexical density , textual information load , high-frequency words , low-frequency words (especially hapax legomena, i.e. the words that are used only once), average word length, average sentence length, average paragraph length and word clusters in the respective corpora. The reader can then read in great details about the contrastive analyses of the lexical features of translational vis-a-vis non-translational Chinese, ranging from the distribution of part-of-speeches, keywords and categories of keywords to such individual case studies as pronouns, connectives, idioms and discourse reformulation markers in Chap. 6 . Following the lexical analyses, Chap. 7 will move on to the exploration of both types of texts, namely, translational or non- translational, at grammatical level in terms of contrastive analyses of a wide range of sentence patterns and grammatical structures such as typical Chinese sentence constructions like the 㻛 bei passive; the disposalᢺ ba construction; the existential ᴹ you sentence; the copulaᱟ shi sentence; the focuserᡰ suo sentence; the 䘎 lian construction as well as quantifi ers; aspect marker s “⵰-zhe , Ҷ-le and 䗷-guo ”; structural auxiliaries “Ⲵ- de , ൠ - de , ᗇ- de and ѻ- zhi ”; and modal particles. Chapter 8 is a summary of the distinct and typical linguistic features of translational Chinese which have been meticulously investigated in the previous three chapters with a discussion of their implications to the translation universals hypotheses . The fi nal chapter of the book combines a conclusive remark of the research with some explor- atory considerations for further studies in the future. Chapter 2 Corpus-Based Translation Studies: An Evolving Paradigm

This chapter discusses the role played by corpus linguistics in contemporary translation studies in past decades. We will begin with an introduction to the fundamental reformative inﬂ uence of corpus linguistics upon the methodologies of the contemporary linguistic research, which is then followed by a more detailed description of the most frequently used types of corpora, particularly multilingual corpora, in contrastive linguistic research and translation studies. We will also introduce a growing paradigm of research by exploring the latest developments in corpus-based translation studies .

2.1 The Corpus “Revolution” in Linguistic Research

Since the 1990s, corpus linguistics has cast a profound reformative even revolution- ary inﬂ uence upon contemporary linguistic research. As Leech ( 1997 : 9) observes, “Corpus analysis has found illuminating usage in almost all branches of linguistics and language learning”. This is not only because corpus linguistics has offered a repertoire of research data and linguistic resource but also because it has offered a new approach or a paradigm of research to linguistics. The corpus-based or corpus- driven methodology has been used in a wide range of linguistic studies, such as lexicography, lexicology, grammar, and register studies, the research of language change and variation, contrastive linguistics, translation studies and so on. More noticeably, many traditional linguistic subdisciplines are gradually turning their ears to corpus-based empirical methods, which could include semantics, pragmatics, stylistics, sociolinguistics, discourse analysis and forensic linguistics (cf. McEnery et al. 2006 ). It is not an exaggeration to remark that since the 1990s, scarcely are there any reference book (e.g. dictionaries and grammar books) compilation projects in existence which do not involve any corpus or corpora whatsoever. As a result, corpus-based research may have already been used even by those who

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 9 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English-Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_2 10 2 Corpus-Based Translation Studies: An Evolving Paradigm have never heard of corpus as a terminology (Hunston 2002 ). Similarly, corpora have become one of the key trajectories that have fundamentally changed our views about language teaching and learning and consequently have been used by an ever- growing number of language teachers and learners (cf. McEnery and Xiao 2010 ; Xiao and Dai 2010b ; Xiao and Xu 2008 ) Nevertheless, in the early 1960s, the prime time of Chomsky’s transformational- generative grammar (TGG or TG) in North America and across the world, the fi rst generation of corpus linguistics which was still in its toddling age had been put under some doubts. It was a period of time when most of the TGG linguistic theorists proved or supported their postulates or theories with examples that were fabricated based on the intuition of the theorist herself. Quirk was among the fi rst criticisers of this approach. Instead, he called upon the importance of authentic linguistic resources with a quotation from Aldous Huxley (1894–1963), “Our most refi ned theories, our most elaborate descriptions are but crude and barbarous simplifi cations of a reality that is, in every smallest sample, infi nitely complex” (from “Vulgarity in Literature”, Music at Night ). The complexity of the actual situation is exactly what Quirk would like to research into (Hu 1992 ; cf. Xu 1997 ). Needless to say, the new generation of corpora and corpus linguistic tools have brought forth enormous changes to the approaches and methods of the contemporary linguistic research, due to which some of our misconceptions toward linguistic rules and the linguistic pursuit in general have been redirected (Xiao 2009b ). The theory-driven versus data-driven distinction in linguistics is a manifestation of the confl ict between rationalism and empiricism in philosophy. The extremist views of these two approaches to linguistics are vividly illustrated by Fillmore’s ( 1992 ) cartoon fi gures of the armchair linguist and the corpus linguist. The armchair linguist thinks that what the corpus linguist is doing is uninteresting, while the corpus linguist believes that what the armchair linguist is doing is untrue. It is hardly surprising that the divorce of theory and empirical data results in either untrue or uninteresting theories because any theory that cannot account for authentic data is a false theory while data without a theory is just a meaningless pile of data. As such, with exceptions of a few extremists from either camp who argue that “corpus linguistics doesn’t mean anything” (Andor 2004 : 97) or that nothing meaningful can be done without a corpus (Murison-Bowie 1996 : 182), the majority of linguists (e.g. Leech 1992 ; Meyer 2002 ; McEnery et al. 2006 ) are aware that the two approaches are complementary to each other. In Fillmore’s ( 1992 : 35) words, “the two kinds of linguists need each other. Or better, […] the two kinds of linguists, wherever possible, should exist in the same body”. In general, in comparison to the traditional linguistic methodology that largely relies on language intuition, the corpus-based methodology would produce more trustworthy results, because the former will usually reject or neglect the use of experimental data, whereas the latter is not against the inclusion of intuitive linguistic data while paying much attention to the empirical analyses. To us, the key to the corpus-based approach is the balance between empirical data and linguistic intuition (Xiao 2009b ). As Leech ( 1991 : 14) observes, since the corpus linguists in the 1950s refused language intuition as the general linguists in the 1960s turned away from corpus data, neither of them have achieved the data coverage and the true knowledge that was achieved by many successful corpus analyses in recent decades. 2.2 Corpora Used in Contrastive Linguistic Research and Translation Studies 11

2.2 Corpora Used in Contrastive Linguistic Research and Translation Studies

There are a variety of corpora that have been used in contrastive linguistic research and translation studies. These corpora can be categorised differently based on different standards, which makes it necessary to clarify the types of corpora useful to our study. In addition, given that the corpus-based translation studies is a relatively new paradigm, it is hardly surprising to ﬁ nd that there is some confusion surrounding the terminology in relation to a diversity of corpora used in translation studies. Before we review the state of the art of corpus-based translation studies, it is necessary and appropriate to clear away some terminological confusions.

2.2.1 Monolingual Versus Multilingual Corpora

As the names suggest, the distinction between monolingual and multilingual corpora is based on the number of languages covered in a corpus. A monolingual corpus literally involves texts of a single language and is primarily designed for intralingual studies. For the purpose of translation studies, a monolingual corpus usually consists of two subcorpora which are created using comparable sampling techniques, with one composed of non-translated native texts and the other of translated texts in the same language. This kind of monolingual corpus is particularly useful in the study of the intrinsic features of translational language. With that said, as we will see shortly, even a simple monolingual corpus of either source or target language alone is useful in translation studies. A multilingual corpus, in contrast, involves texts of more than one language. As corpora that cover two languages are conventionally known as “bilingual”, multilingual corpora, in a narrow sense, must involve more than two languages, though “multilingual” and “bilingual” are often used interchangeably in the literature and also in this chapter. A multilingual corpus can be a parallel corpus or a comparable corpus (see below). Both types are useful in translation studies, while a comparable corpus is also particularly useful in crosslinguistic contrast.

2.2.2 Parallel Versus Comparable Corpora

It can be said that terminological confusion in multilingual corpora centres on these two terms. For some scholars (e.g. Aijmer et al. 1996 ; Granger 1996 : 38), corpora composed of source texts in one language and their translations in another language (or other languages) are “translation corpora”, while those comprising different components sampled from different native languages using comparable sampling techniques are called “parallel corpora”. 12 2 Corpus-Based Translation Studies: An Evolving Paradigm

For others (e.g. Baker 1993 : 248, 1995 , 1999 ; Barlow 1995 , 2000 : 110; Hunston 2002 : 15; McEnery and Wilson 1996 : 57; McEnery et al. 2006 ), corpora of the fi rst type are labelled “parallel”, while those of the latter type are comparable corpora. As argued in McEnery and Xiao (2007a ), while different criteria can be used to defi ne different types of corpora, they must be used consistently and logically. For example, we can say a corpus is monolingual, bilingual or multilingual if we take the number of languages involved as the criterion for defi nition. We can also say a corpus is a translation or a non-translation corpus if the criterion of corpus content is used. But if we choose to defi ne corpus types by the criterion of corpus form, we must use it consistently. Then we can say a corpus is parallel if the corpus contains source texts and translations in parallel, or it is a comparable corpus if its subcorpora are comparable by applying the same sampling frame. It is illogical, however, to refer to corpora of the fi rst type as translation corpora by the criterion of content while referring to corpora of the latter type as comparable corpora by the criterion of form. A parallel corpus, in our terms, can be either unidirectional (e.g. from English into Chinese or from Chinese into English alone) or bidirectional (e.g. containing both English source texts with their Chinese translations and Chinese source texts with their English translations) or multidirectional (e.g. the same piece of text with its Chinese, English, French, Russian and Arabic versions). In this sense, texts that are produced simultaneously in different languages (e.g. UN regulations) also belong to the category of parallel corpora. A parallel corpus must be aligned at a certain level (for instances, at document, paragraph, sentence or word level) in order to be useful in translation studies. Yet the automatic alignment of parallel corpora is not a trivial task for some language pairs, though alignment is generally very reliable for many closely related European language pairs (cf. McEnery et al. 2006 ).

2.2.3 Comparable Versus Comparative Corpora

Another complication in terminology involves a corpus which is composed of different varieties of the same language. This is particularly relevant to translation studies because it is a very common practice in this research area to compare a corpus of translated texts—which we call a “translational corpus”—and a corpus consisting of comparably sampled non-translated texts in the same language. They form a monolingual comparable corpus . To us, a multilingual comparable corpus samples different native languages, with its comparability lying in the matching or comparable sampling techniques, similar balance (i.e. coverage of genres and domains) and representativeness and similar sampling period. By our defi nition, corpora containing different varieties of the same language (e.g. the International Corpus of English) are not comparable corpora because all corpora, as a resource for linguistic research, have “always been pre-eminently suited for comparative studies” (Aarts 1998 : ix), either intralingual or interlingual. The Brown , LOB , Frown and FLOB corpora are also used typically for 2.2 Corpora Used in Contrastive Linguistic Research and Translation Studies 13 comparing language varieties synchronically and diachronically. Corpora like these can be labelled as “comparative corpora”. They are not “comparable corpora” as suggested in the literature (e.g. Hunston 2002 : 15). Having clarifi ed some terminological confusion in corpus-based translation studies, it is worth pointing out that the distinctions discussed here are purely for the sake of clarifi cation. In reality, there are multilingual corpora which are a mixture of parallel and comparable corpora. For example, the English-Norwegian Parallel Corpus (ENPC) can be considered as a combination of parallel and comparable corpus albeit its name.

2.2.4 General Versus Specialised Corpora

General and specialised corpora differ in terms of coverage, i.e. the range of genres and domains they are supposed to represent. As general corpora such as the British National Corpus (BNC ) “typically serve as a basis for an overall description of a language or language variety” (McEnery et al. 2006 : 15), they tend to proportionally cover as many genres and domains as practically possible to ensure maximum balance and representativeness. Specialised corpora, on the other hand, tend to be composed of texts from a specifi c domain (e.g. engineering or business) or genre (e.g. news text or academic writing). While balanced general corpora are undoubtedly very useful in translation research, as in many other areas, specialised corpora are of exceptional value for translation studies. This is because specialised corpora are rich in terminology; they have practical value to translators of technical texts; and they can provide a basis for studying the authorship of original texts, translatorship or any other traces of the many “pragmatic texts” (cf. Baker 1995 : 225–226). Both monolingual and multilingual corpora, including parallel and comparable corpora, can be of specialised or general corpus type, depending on their purposes. For example, for exploring how general linguistic features such as tense and aspect markers are translated, balanced corpora, which are supposed to be more representative of any given language in general, would be used; for extracting terminologies or neologies, specialised parallel and comparable corpora are clearly of better use. However, since it is relatively easier to fi nd comparable text categories in different languages, it is more likely for comparable corpora to be designed as general balanced corpora in relation to parallel corpora. Corpora of different kinds can be used for different purposes in translation studies. For example, parallel corpora are useful in exploring how an idea in one language is conveyed in another language, thus providing indirect evidence to the study of translation process. Corpora of this kind are indispensable for building statistical or example-based machine translation (EBMT) systems and for the development of bilingual lexicons and translation memories. Also, parallel concordance is a useful tool for translators. Comparable corpora are useful in improving translator’s subject fi eld understanding and improving the quality of translation in terms of fl uency, cor- 14 2 Corpus-Based Translation Studies: An Evolving Paradigm rect term choice and idiomatic expressions in the chosen fi eld. They can also be used to build terminology banks. Translational corpora provide primary evidence in product-oriented translation studies and in studies of translation universals. If corpora of this kind are encoded with sociolinguistic and cultural parameters, they can also be used to study the sociocultural environment of translations. Even monolingual corpora of source language and target language are of great value in translation studies because they can raise the translator’s linguistic and cultural awareness in general and provide a useful and effective reference tool for translators and trainees. They can also be used in combination with a parallel corpus to form a so-called translation evaluation corpus that helps translator trainers or critics to evaluate translations more effectively and objectively.

2.3 Corpus-Based Translation Studies: The State of the Art

The application of corpora to translation studies is not limited just to the descriptive or theoretical pursuit of the discipline, but rather extends widely to various aspects of translating. For example, corpora have been used in translation training (such as curriculum design, translation teaching, teaching assessment, etc.); they have been taken as the foundation of many translation enhancement systems ranging from the compilation of paperback or online references or dictionaries to the creation of machine translation (MT) systems, computer-aided translation (CAT) systems and translation memory (TM) or term banks; corpora have also found their use in translation criticism and translation quality assessment; more recent studies show that they can be helpful to other forms of translating, e.g. visual-audio translating, software localisation and webpage translating, and even to the strategic decision-making of global advertising. This section will review the up-to-date state of the art of the corpus-based translation studies by following the Holmes-Toury map, i.e. applied TS, descriptive TS and theoretical TS.

2.3.1 Applied Translation Studies

On the applied TS front, three major contributions of corpora include corpus- assisted translating, corpus-aided translation teaching and training and development of translation tools. An increasing number of studies have demonstrated the value of corpora, corpus linguistic techniques and tools in assisting translation production, translator training and translation evaluation. For example, Bernardini ( 1997 ) suggests that “large corpora concordancing” (LCC) can help students to develop “awareness”, “reﬂ ectiveness” and “resourcefulness” and the skills which distinguish a translator from those unskilled amateurs. Bowker (1998 : 631) observes that “corpus-assisted translations are of a higher quality with respect to subject ﬁ eld understanding, correct term choice and idiomatic expressions”. 2.3 Corpus-Based Translation Studies: The State of the Art 15

Zanettin (1998 ) shows corpora help trainee translators become aware of general patterns and preferred way of expressing things in the target language, get better comprehension of source language texts and improve production skills; Aston ( 1999 ) demonstrates how the use of corpora can enable translators to produce more native-like interpretations and strategies in source and target texts, respectively; according to Bowker ( 2001 ), an evaluation corpus, which is composed of a parallel corpus and comparable corpora of source and target languages, can help translator trainers to evaluate student translations and provide more objective feedback; Bernardini ( 2002a ), Hansen and Teich ( 2002 ) and Tagnin ( 2002 ) show that the use of multilingual concordancer in conjunction with parallel corpora can help students with “a range of translation-related tasks, such as identifying more appropriate target language equivalents and collocations, identifying the norms, stylistic preferences and discourse structures associated with different text types, and uncovering important conceptual information” (Bowker and Barlow 2004 : 74); Bernardini and Zanettin (2004 ) suggest corpora be used in order to “provide a framework within which textual and linguistic features of translation can be evaluated”. Vintar ( 2007 ) reports their efforts to build up Slovene corpora for translator training and practice. Corpora, and especially aligned parallel corpora, are essential for the development of translation technology such as machine translation (MT) systems and computer-aided translation (CAT) tools. MT is designed to translate without or with minimal human intervention. MT systems have become more reliable since the methodological shift in the 1990s from rule-based to text-based algorithms which are enhanced by statistical models trained using corpus data. Parallel corpora can be said to play an essential role in developing example-based and statistical MT systems. Well-known MT systems include examples such as Systran, Babelﬁ sh, World Lingo and Google Translation. MT systems like these are mainly used in translation of domain-speciﬁ c and controlled language, auto- mated “gisting” of online contents and translation of corporate communica- tions, locating text or fragments requiring human translation. CAT tools are designed to assist in human translation. There are three major types of CAT tools. Translation memory and terminology management tools are the most important type. They can be used to create, manage and access translation memories (TMs) and termbases. They can also suggest translation candidates intel- ligently in the process of translation. A second type is localisation tools, which are able to distinguish program codes or tags from the texts to be translated (e.g. menus, buttons, error messages, etc.) or, even better, turn the program codes or tags into what a program or webpage really looks. Another type of tool is used in audiovisual translation (e.g. subtitling, dubbing and voice-over). Major products of CAT tools include SDL Trados, Deja Vu, Transit and Wordfast for TM and terminology tools, Catalyst for software localisation, Trados TagEditor for webpage translation and WinCap for subtitling. CAT tools have brought translation into the industrial age, but they are useless unless translated units and terminologies have been stored in translation memories and termbases. This is where corpora come into the picture. 16 2 Corpus-Based Translation Studies: An Evolving Paradigm

2.3.2 Descriptive Translation Studies

Descriptive translation studies (DTS ) is characterised by its emphasis on the study of translation per se. It is to answer the question of “why a translator translates in this way” instead of dealing with the problem of “how to translate” (Holmes 1972 /1988). The target-oriented and empirical nature of the methodology is in perfect harmony with DTS. Baker ( 1993 : 243) predicted that the availability of large corpora of both source and translated texts, together with the development of the corpus-based approach, would enable translation scholars to uncover the nature of translation as a mediated communicative event. Corpus-based DTS has revealed its full vitality over the past decade, which will be reviewed in this section in terms of its three foci—translation as a product, translation as a process and the function of translation (Holmes 1972 /1988). 1 . Product-oriented DTS Corpus-based DTS has primarily been concerned with describing translation as a product, by comparing corpora of translated and non-translated native texts in the target language , especially translated and native English. The majority of product- oriented translation studies attempt to uncover evidence to support or reject the so- called translation universals hypotheses . As far as the English language is concerned, a large part of product-oriented translation studies have been based on the Translational English Corpus (TEC), which was built by Mona Baker and her colleagues at the University of Manchester. The TEC, which was designed specifi cally for the purposes of studying translated texts, consists of contemporary written texts translated into English from a range of source languages. It is constantly expanded with fresh materials, reaching a total of 20 million words by 2001. The corpus comprises full texts from four genres (fi ction, biography, newspaper articles and in-fl ight magazines) translated by native speakers of English. Paralinguistic data such as the information of translators, source texts and publishing dates are annotated and stored in the header section of each text. A subcorpus of original English was specifi cally selected and is being modifi ed from the British National Corpus (BNC) to match the TEC in terms of both composition and dates of publication. Presently, the TEC is perhaps the only publicly available corpus of translational English. Most of the pioneering and prominent studies of translational English have been based on this corpus, which have so far focused on syntactic and lexical features of translated and non-translated texts of English. They have provided evidence to support the hypotheses of translation universals , e.g. simplifi cation , explicitation , sanitisation and normalisation . For example, Laviosa ( 1998b ) studies the distinctive features of translational English in relation to native English (as represented by the British National Corpus ), fi nding that translational language has four core patterns of lexical use: a relatively lower proportion of lexical words over function words, a relatively higher proportion of high-frequency words over low-frequency words, a relatively greater repetition of the most frequent words and a smaller vocabulary frequently used (see Sect. 3.1.2 for further discussion). This is regarded as the most 2.3 Corpus-Based Translation Studies: The State of the Art 17 signifi cant work in support of the simplifi cation hypothesis of translation universals. Olohan and Baker’s (2000 ) comparison of concordances from the TEC and the BNC shows that the that connective with reporting verbs say and tell is far more frequent in translational English and, conversely, that the zero connective is more frequent in native English. These results provide strong evidence for syntactic explicitation in translated English, which, unlike “the addition of explanatory information used to fi ll in knowledge gaps between source text and target text readers, is hypothesized to be a subliminal phenomenon inherent in the translation process” (Laviosa 2002 : 68). Olohan ( 2004 ) investigates intensifi ers such as quite , rather , pretty and fairly in translated versus native English fi ction in an attempt to uncover the relation between collocation and moderation, fi nding that pretty and rather , and more marginally quite , are considerably less frequent in the TEC-fi ction subcorpus, but when they are used, there is usually more variation in usage, and less repetition of common collocates, than in the BNC-fi ction corpus. A number of corpus-based studies have explored lexical patterning in translational language. For example, Kanter et al. (2006 ) identify new universals charac- terising the mutual overlaps between native English and translated English on the basis of Zipf’s law. Øverås (1998 ) explores the relationship between collocation and explicitation in English and Norwegian novels, demonstrating how a collocational clash in the source text is translated using a conventional combination in the target language . Kenny ( 2001 ) studies the relationship between collocation and sanitisation on the basis of an English-German parallel corpus and monolingual corpora of source languages. Baroni and Bernardini ( 2003 ) compare the bigrams (i.e. two- word clusters) in a monolingual comparable corpus of native Italian texts and translated articles from a geopolitics journal, concluding that Translated language is repetitive, possibly more repetitive than original language. Yet the two differ in what they tend to repeat: translations show a tendency to repeat structural patterns and strongly topic-dependent sequences, whereas originals show a higher incidence of topic-independent sequences, i.e. the more usual lexicalized collocations in the language. (Baroni and Bernardini 2003 : 379) One interesting area of product-oriented translation research involves corpora composed of multiple translations of the same source text for comparing individual styles of translators. One such corpus is the Hong Lou Meng Parallel Corpus , which is composed of the Chinese original and four English translations of the classic Chinese novel, Hong Lou Meng “Dream of the Red Chamber ” (cf. Liu 2010 ). 2 . Process-oriented DTS Process-oriented DTS aims at revealing the thought processes that take place in the mind of the translator while she or he is translating. While it is diffi cult to study those processes online, one possible way for corpus-based DTS is to investigate into the written transcripts of these recordings offl ine, which is known as think-aloud protocols (or TAPs; see Bernardini 2002b ). However, the process cannot be totally detached from the product. Stubbs (2001a ) draws parallels between corpus linguistics and geology, both assuming a relation between process and product. A geologist is interested in geological processes, which are not directly observable, but 18 2 Corpus-Based Translation Studies: An Evolving Paradigm

individual rocks and geographical formations are observable traces of geological processes such as destruction and construction. As such, as Stubbs ( 2001a : 154) agues, “By and large, the processes are invisible, and must be inferred from the products”. The same can be said of translation: translation as a product can provide indirect evidence of translation as a process. Hence, both types of studies can be approached on the basis of corpus data. Process-oriented studies are typically based on parallel corpora by comparing source and target texts, while product-oriented studies are usually based on monolingual comparable corpora by comparing translated target language and native target language. For example, Utka (2004 ) is a process-oriented study based on the English-Lithuanian Phases of Translation Corpus . Quantitative and qualitative comparisons of successive draft versions of translation have allowed him not only to reject Toury’s (1995 ) claim that it is impos- sible to use corpus to study translation process but also to report cases of normalisation, systematic replacement of terminology and infl uence by the original language. Chen ( 2006 ) also presents a corpus-based study of connective s, namely, conjunction s and sentential adverbials, in a “composite corpus” composed of English source texts and their two Chinese versions independently produced in Taiwan and mainland China, plus a comparable component of native Chinese texts as the reference corpus in the genre of popular science writing. This investigation integrates product- and process-oriented approaches in an attempt to verify the hypothesis of explicitation in translated Chinese. In the product-oriented part of his study, Chen compares translational and native Chinese texts to fi nd out whether connectives are signifi cantly more common in the fi rst type of texts in terms of parameters such as frequency, type/token ratio (TTR ) as well as statistically defi ned common connectives and the so-called translational distinctive connectives (TDCs) and whether syntactic patterning in the translated texts is different from native texts via a case study of fi ve TDCs that are most signifi cant statistically. In the process-oriented part of the study, he compares translated Chinese texts with the English source texts, through a study of the same fi ve TDCs, in an attempt to determine the extent to which connectives in translated Chinese texts are carried over from the English source texts or, in other words, the extent to which connective s are explicated in translational Chinese. Both parts of his study support the hypothesis of explicitation as a translation universal in the process and product of English-Chinese translation of popular science writing. 3 . Function-oriented DTS Function-oriented DTS encompasses research which describes the function or impact that a translation or a collection of translations may have in the sociocultural context of the target language, thus leading to the “study of contexts rather than texts” (Holmes 1972 /1988: 72). There are relatively few function-oriented studies that are corpus based, possibly because the marriage between corpora and this type of research, just like corpus-based discourse analysis (e.g. Baker 2006 ), is still in the “honeymoon” period. 2.3 Corpus-Based Translation Studies: The State of the Art 19

One such study is Laviosa ( 2000 ), which is concerned with the lexicogrammatical analysis of fi ve semantically related words (i.e. Europe , European , European Union , Union and EU ) in the TEC. These words are frequently used in translated newspaper articles and can be considered as what Stubbs (1996 , 2001b ) call “cultural keywords”, or words that are important from a sociocultural point of view, because they embody social values and transmit culture, and reveal the image of Europe as portrayed by the data from translated articles in The Guardian and The European. Given that the TEC is a growing multisource-language corpus of translational English, Laviosa (2000 ) suggests that it is possible to carry out comparative analyses between Europe and other lemmas of cultural keywords such as Britain and British , France and French , Italy and Italian, etc., which may lead to the direction of corpus-based investigation into the ideological impact of translated texts. Similarly, Baker (2000 ) examines, on the basis of the fi ctional component of the TEC, three aspects of linguistic patterning in the works of two British literary translators, i.e. average sentence length, type/token ratio and indirect speech with the typical reporting verbs such as say . The results indicate that the two translators differ in terms of their choices of source texts and intended readership for the translated works. One translator is found to prefer works targeting at a highly educated readership with an elaborate narrative which creates a world of intellectually sophisticated characters. In contrast, the other chooses to translate texts which are less elaborate in narrative and concerned with emotions for an ordinary readership. These fi ndings allow Baker ( 2000 ) to draw the conclusion that it is “also possible to use the description emerging from a study of this type to elaborate the kind of text world that each translator has chosen to recreate in the target language ” (cf. Kruger 2002 : 96). Kruger (2000 ) examines whether the Afrikaans “stage translation” of The Merchant of Venice reveals more spoken language features signalling involvement and interaction between the characters than a “page translation”. He used an analytical tool that would not only enable him to quantify linguistic features of involvement in four Shakespeare texts (the original and three translations) but also provide a “norm” of the occurrence of such features in authentic spoken English. This type of investigation allows him to validate his assumptions that different registers of translated drama have different functions and therefore they present information differently. Masubelele (2004 ) compares the 1959 and 1986 translations of the Book of Matthew into Zulu in a translational corpus to research into the role played by Bible translation in the growth and development of written Zulu in the context of South Africa, aiming to examine the changes in the orthography, phonology, morphology, syntax, lexis and register of Zulu brought about by the translation works. She fi nds that Toury’s ( 1980 ) concept of the initial norm (i.e. the sociocultural constraints) “seems to have guided the translators of these translations in their selection of the options at their disposal” (Masubelele 2004 : 201). The study shows “an inclination towards the target norms and culture”—while the translators of the 1959 version adopted source text norms and culture, the translators of the 1986 version adopted the norms of the target culture (ibid: 201). 20 2 Corpus-Based Translation Studies: An Evolving Paradigm

2.3.3 Theoretical Translation Studies

Theoretical translation studies aims “to establish general principles by means of which these phenomena can be explained and predicted” ( Holmes 1988: 71). It elaborates principles, theories and models to explain and predict what the process of translation is, given certain conditions such as a particular pair of languages or a particular pair of texts. Unsurprisingly it is closely related to, and is often reliant on, the empirical fi ndings produced by descriptive translation studies . One good battleground of using DTS fi ndings to pursue general theory of translation is the hypotheses of the so-called translation universals ( TUs ), which are sometimes referred to as the inherent features of translational language or “ translationese ”. It is a well-recognised fact that translations cannot possibly avoid the effect of translationese (cf. Hartmann 1985 ; Baker 1993 : 243–245; Teubert 1996 : 247; Gellerstam 1996 ; Laviosa 1997 : 315; McEnery and Wilson 2001 : 71–72; McEnery and Xiao 2002 , 2007a ). The concept of TUs is fi rst proposed by Baker ( 1993 ), who suggests that all translations are likely to show certain linguistic characteristics simply by virtue of being translations, which are caused in and by the process of translation. The effect of the source language on the translations is strong enough to make the translated language perceptibly different from the target native language. Consequently translational language is at best an unrepresentative special variant of the target language (McEnery and Xiao 2007a ). The distinctive features of translational language can be identifi ed by comparing translations with comparable native texts, thus throwing new light on the translation process and helping to uncover the translation norms, or what Frawley ( 1984 ) calls the “ Third Code ” of translation. Over the past decade, TUs have been an important area of research in descriptive translation studies as well as a target of hot debates. Some scholars (e.g. Tymoczko 1998 ) argue that the very idea of making universal claims about translation is inconceivable, while others (e.g. Toury 2004 ) advocate that the chief value of general laws of translation lies in their explanatory power; still others (e.g. Chesterman 2004 ) accept universals as one possible route to high-level generalisations. Chesterman (2004 ) further differentiates two types of TUs : one relates to the process from the source to the target text (what he calls “S-universals ”), while the other (“T-universals ”) compares translations to other target-language texts. Mauranen ( 2007 ) suggests in her comprehensive review of TUs that the discussions on TUs follow the general discussion on “universals” in language typology. Recent corpus-based works have proposed a number of TUs , the best known of which include explicitation , simplifi cation , normalisation , sanitisation and levelling out (or convergence ). Other TUs that have been investigated include under- representation, interference and untypical collocations (see Mauranen 2007 ). While a study can investigate more than one of these features, they will be discussed in the following chapter separately for the purpose of a clearer presentation. Chapter 3 Exploring the Features of Translational Language

As discussed in the previous chapter, the convergence between corpus linguistics and translation studies since the early 1990s has greatly facilitated what Toury ( 1995 ) calls “product-oriented translation research”, helping to bring up systematic methodologies and trustworthy empirical data to the discipline. One of the most important topics in contemporary descriptive translation studies is the discussion of translation universals ( TUs ) and the related hypotheses, i.e. the exploration of the typical features of translational language as a linguistic variant in existence in itself. As observed by Hansen and Teich (2001 : 44), “it is commonly assumed in translation studies that translations are specifi c kinds of texts that are not only different from their original source language (SL) texts, but also from comparable original texts in the same language as the target language (TL)”. Their observation, in general, has been supported by many corpus-based studies which give evidence of the linguistic features that differentiate the translated texts from the SL as well as from the TL native writings. It seems widely recognised that translations cannot possibly avoid the effect of translationese (cf. Hartmann 1985 ; Baker 1993 : 243–245; Teubert 1996 : 247; Gellerstam 1996 ; Laviosa 1997 : 315; McEnery and Wilson 2001 : 71–72; McEnery and Xiao 2002 , 2007b ). The distinctive features of translational language can be identifi ed by comparing translations with comparable native texts, thus casting new light on the translation process and helping to uncover translation norms, or what Frawley (1984 ) calls the “Third Code” of translation. In reality, the features of translation in the current corpus- based translation studies have been differently expressed in such seemingly debateable terms as translation universals or the universal features of translation. The later terms, arguable in some sense, suggest a very strong endorsement that there are some special properties in the translated text that are the result of the translating process per se irrespective of the specifi c language pairs that translating involves. Admittedly, the TUs hypotheses were questioned by some theorists, which makes a comprehensive up-to- date review of the theories and empirical studies on those hypotheses undoubtedly necessary for further discussions on any particular translational language.

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 21 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English-Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_3 22 3 Exploring the Features of Translational Language

3.1 The Translation Universals Hypotheses

While Frawley ( 1984 ) recognised translated language as a distinct language variant, it is since Baker’s seminal paper (1993 ) that “the idea of linguistic translation universals has found a place at the centre of discussion in translation studies” (Mauranen and Kujamäki 2004 : 1). Baker (1993 ) suggests that all translations are likely to show certain linguistic characteristics simply by virtue of being translations, which are caused in and by the process of translation. The effect of the source language on the translations is strong enough to make the translated language perceptibly different from the target native language. Consequently translational language is at best an unrepresentative special variant of the target language ( McEnery and Xiao 2007b ). Over the past decade, TUs have been an important area of research as well as a target of debate in descriptive translation studies. Some scholars (e.g. Tymoczko 1998 ; Malmkjær 2005 ; House 2008 ) are sceptical of translation universals, arguing that the very idea of making universal claims about translation is inconceivable, while others (e.g. Toury 2004 ) advocate that the chief value of general laws of translation lies in their explanatory power; still others (e.g. Chesterman 2004 ) accept universals as one possible route to high-level generalisations. According to Chesterman (2004 : 39), translation universals are higher-level generalisations of the common properties of translated texts, which can be either “universal differences between translations and their source texts, i.e. characteristics of the way in which translators process the source text” (i.e. S-universals ), or “universal differences between translations and comparable non-translated texts, i.e. characteristics of the way translators use the target language” (i.e. T-universals ). Mauranen (2007 ) suggests in her comprehensive review of TUs that the discussion of TUs should follow the general discussion on “universals” in language typology. Recent corpus-based translation studies have proposed a number of TUs, the best known of which include explicitation , simpliﬁ cation , normalisation , sanitisation and levelling out (or convergence). Other TUs that have been investigated include Source Language shining through (Teich 2001 ), under- representation , interference and untypical collocations (cf. Mauranen 2007 ). Explicitation and SL interference are considered to be S-universals , whereas simpliﬁ cation , normalisation and TL unique item under- representation can be taken as T-universals , while levelling out can be either. We will review these key concepts and studies related so as to set the scene for the research presented in the following chapters.

3.1.1 Explicitation

The explicitation hypothesis is formulated by Blum-Kulka (1986 ) on the basis of evidence from individual sample texts showing that translators tend to make explicit optional cohesive markers in the target text even though they are absent in the source text. It relates to the tendency in translations to “spell things out rather 3.1 The Translation Universals Hypotheses 23 than leave them implicit” (Baker 1996 : 180). Explicitation can be realised syntactically or lexically, for instance, via more frequent use of conjunction s in translated texts than in non-translated texts. One result of explicitation is increased cohesion in translated text (Øverås 1998 ). Pym ( 2005 ) provides an excellent account of explicitation, locating its origin, discussing its different types, elaborating a model of explicitation within a risk-management framework and offering a range of explanations of the phenomenon. In the light of the distinction made above between S- and T-universals (Chesterman 2004 ), explicitation would seem to fall most naturally into the S-type. Recently, however, explicitation has also been studied as a T-universal. In his corpus-based study of structures involving NP modiﬁ cation (i.e. equivalent of the structure noun + prepositional phrase in English) in English and Hungarian, Varadi (2007 ) suggests that gen- uine cases of explicitation must be distinguished from constructions that require expansion in order to meet the requirements of grammar (see House’s 2008 distinction between optional and obligatory linguistic choices). While explicitation is found at various linguistic levels ranging from lexis to syntax and textual organisation, “there is variation even in these results, which could be explained in terms of the level of language studied, or the genre of the texts” (Mauranen 2007 : 39). The question of whether explicitation is a translation universal is yet to be conclusively answered, according to existing evidence which has largely come from translational English and related European languages.

3.1.2 Simpliﬁ cation

Simplifi cation refers to “the tendency to simplify the language used in translation” (Baker 1996 : 181–182), which means that translational language is supposed to be simpler than native language, lexically, syntactically and/or stylistically (cf. Blum- Kulka and Levenston 1983 ; Laviosa-Braithwaite 1997 ). As noted earlier, product- oriented studies such as Laviosa (1998b ) and Olohan and Baker (2000 ) have provided evidence for lexical and syntactic simplifi cation in translational English. Translated texts have also been found to be simplifi ed stylistically. For example, Malmkjær (1997 ) notes that in translations, punctuation usually becomes stronger; for example, commas are often replaced with semicolons or full stops while semicolons are replaced with full stops. As a result, long and complex sentences in the source text tend to be broken up into shorter and less complex clauses in translations (a phenomenon that Fabricius-Hansen 1999 refers to as “sentence splitting”), thereby reducing structural complexity for easier reading. On the other hand, Laviosa (1998b : 5) observes that translated language has a signifi cantly greater mean sentence length than non-translated language. Xiao and Yue’s ( 2009 ) fi nding that translated Chinese fi ction displays a signifi cantly greater mean sentence length than native Chinese fi ction is in line with Laviosa’s ( 1998b : 5) observation but goes against Malmkjær’s ( 1997 ) expectation that stronger punctuations tend to result in shorter sentences in translated texts. It appears, then, that mean 24 3 Exploring the Features of Translational Language sentence length might not be a translation universal but rather associated with specifi c languages or genres. The simplifi cation hypothesis, however, is controversial. It has been contested by subsequent studies of collocations (Mauranen 2000 ), lexical use (Jantunen 2001 ) and syntax (Jantunen 2004 ). Just as Laviosa-Braithwaite (1996 : 534) cautions, evidence produced in early studies that support the simplifi cation hypothesis is patchy and not always coherent. Such studies are based on different datasets and are carried out to address different research questions and thus cannot be compared.

3.1.3 Normalisation

Normalisation , also known as “conventionalisation” in the literature (e.g. Mauranen 2007 ), refers to the “tendency to exaggerate features of the target language and to conform to its typical patterns” (Baker 1996 : 183). As a result, translational language appears to be “more normal” than the target language. Typical manifestations of normalisation include overuse of cliches or typical grammatical structures of the target language, overuse of typical features of the genres involved, adapting punctuation to the typical usage of the target language and the treatment of the different dialects used by certain characters in dialogues in the source texts. Kenny ( 1998 , 1999 , 2000 , 2001 ) presents a series of studies of how unusual and marked compounds and collocations in German literary texts are translated into English, in an attempt to assess whether they are normalised by means of more conventional use. Her research suggests that certain translators may be more inclined to normalise than others and that normalisation may apply in particular to lexis in the source text. Nevalainen ( 2005 , cited in Mauranen 2007 : 41) suggests that translated texts show greater proportions of recurrent lexical bundles or word clusters . Beyond the lexical level, there are a number of studies which explore grammatical normalisation (e.g. Teich 2001 ; Hansen 2003 ). Like simpliﬁ cation, normalisation is also a debatable hypothesis. According to Toury ( 1995 : 208), it is a “well-documented fact that in translations, linguistic forms and structures often occur which are rarely, or perhaps even never encountered in utterances originally composed in the target language ”. Tirkkonen-Condit’s ( 2002 : 216) experiment, which asked subjects to distinguish translations from non- translated texts, also shows that “translations are not readily distinguishable from original writing on account of their linguistic features”.

3.1.4 Other Translation Universals Hypotheses

Kenny (1998 ) analyses semantic prosody (i.e. a kind of meaning arising from collocation) in translated texts in an attempt to ﬁ nd evidence of sanitisation (i.e. reduced connotational meaning). She concludes that translated texts are “somewhat ‘sani- tized’ versions of the original” (Kenny 1998 : 515). 3.2 The State of the Art of Corpus-Based Translation Studies in Chinese 25

Another translation universal that has been proposed is the so-called feature of “levelling out”, i.e. “the tendency of translated text to gravitate towards the centre of a continuum” (Baker 1996 : 184). This is what Laviosa ( 2002 : 72) calls “ convergence”, i.e. the “relatively higher level of homogeneity of translated texts with regard to their own scores on given measures of universal features” that are discussed above. “Under- representation ”, which is also known as the “unique items hypothesis”, is concerned with the unique items in translation (Mauranen 2007 : 41–42). For example, Tirkkonen-Condit ( 2005 ) compared the frequencies and uses of the clitic particle kin in translated and original Finnish in fi ve genres (i.e. fi ction, children’s fi ction, popular fi ction, academic prose and popular science), fi nding that the average frequency of kin in original Finnish is 6.1 instances per 1,000 words, whereas its normalised frequency in translated Finnish is 4.6 instances per 1,000 words. Tirkkonen-Condit interprets this result as a case of under- representation in translated Finnish. Aijmer’s (2007b ) study of the use of the English discourse marker oh and its translation in Swedish shows that there is no single lexical equivalent of oh in Swedish translation, because direct translation with the standard Swedish equivalent ah would result in an unnatural sounding structure in this language. Another feature of translational language is Source Language shining through, which means that “[i]n a translation into a given target language (TL), the translation may be oriented more towards the source language (SL), i.e. the SL shines through” (Teich 2003 : 145). For example, Teich (2003 : 207) fi nds that in both English-to-German and German-to-English translations, both target languages exhibit a mixture of TL normalisation and SL shining through. The above is not a comprehensive survey of translation universals. There are still some other features of translations that are often discussed in translation textbooks as strategies of translation (e.g. expansion), which will not be reviewed here.

3.2 The State of the Art of Corpus-Based Translation Studies in Chinese

Clearly this book aims to investigate the linguistic features of translational Chinese in English-Chinese translating, which is largely carried out within the theoretical framework of translation universals hypotheses discussed in the previous section. Most of the studies known to the academia are based on contrastive analyses between Chinese translated texts and the comparable native texts with the aim of ﬁ nding the distinctive linguistic characteristics of translational Chinese in contrast to non-translational or native Chinese. The principal trajectory behind these studies is the development and promotion of Chinese corpora, particularly the parallel and comparable corpora of Chinese texts since the 1980s. Accordingly, corpus-based studies of Chinese translations are usually accompanied by the construction of Chinese parallel and comparable corpora. For example, the studies carried out by Kefei Wang and his colleagues (e.g. Ke 2003 , 2005 ; Qin and Wang 2004 ; Huang 2007 ; Wang and Qin 2010) are all based on the General 26 3 Exploring the Features of Translational Language

Chinese-English Parallel Corpus (GCEPC) which is initiated, built and maintained at Beijing Foreign Studies University. Other similar studies are also dependent on a variety of corpora built by research institutions or individual researchers. For instance, Chen (2004 , 2006 and 2007 ) investigated the explicitation of connective s in translations of popular science based on a self-built 1.8-million-word English-Chinese Parallel Corpus consisting of mostly popular science texts in both English and Chinese and the Academia Sinica Balanced Corpus of Modern Chinese (the Sinica Corpus for short). Hu ( 2007 ) carried out a descriptive study of the lexical and syntactical features of Chinese translated novels based on the Contemporary Chinese Translated Fiction Corpus built by the researcher himself. The explicitation features of the two Chinese versions of Hamlet discussed by Hu and Zhu ( 2008 ) rely on a translational corpus of Shakespeare’s drama built by Shanghai Jiaotong University. It is fair, therefore, to conclude that the growing number of corpus-based translation studies carried out by Chinese translation theorists will not be possible without the development of corpus linguistic techniques in the beginning of this century. In terms of the approaches of these corpus-based studies, a majority of them have been done under the same framework of translation universals hypotheses, among which the hypothesis of explicitation is the most frequently discussed topic among Chinese theorists. For example, He (2003 ) chose The Last Leaf, a short story by the nineteenth-century American writer, O. Henry, and its Chinese translation to analyse the explicitness of the translation. The contrastive analysis shows that 79 out of the 134 sentences (58.96 %) in the source text were translated more or less explicitly. Following the observation of Øverås ( 1998 ), He ( 2003 ) also endorsed the existence of explicitation in English-Chinese translation, either in terms of “spelling out” the implicit ST information (e.g. additional expressions, specifi cation) or in terms of the increased cohesion in the TL (e.g. shift of person, reconstruction of sentence and paragraph, standardisation, and shift of fi gure-of-speech). More detailed discussion of the explicitation techniques in English-Chinese translation was also given by Ke (2005 ), which, based on a large number of examples in English-Chinese and Chinese- English translating, came up with a reasonable conclusion that explicitation does exist in the translation between the two major languages. While discussing the “lexical operational norms”, or the general tendency of lexical features of translated contemporary Chinese novels, Hu ( 2006 : 135) offered a set of similar lexical “core patterns” in Chinese literary translation as reported by Laviosa ( 1998b ). In contrast to non-translated Chinese novels and Chinese native texts in general, Chinese translated novels have presented (a) a smaller type/token ratio , (b) a lower lexical density, (c) a larger number and higher frequency of high- frequency (i.e. the most frequently used) words and (d) a greater proportion of grammatical words. Some of these lexical patterns were regarded as evidence of explicitation of Chinese literary translation. Similarly, Chen ( 2006 ) investigated the use of connective s in English and Chinese translations of popular science with the explicit aim to testify the explicitation universal. Another study carried out by Huang ( 2007 ) on the connectives and personal pronoun s in Chinese translations was also focussed on the same explicitation hypothesis. Two types of explicitation were 3.3 Specifi c Research in the Linguistic Features of Translational Chinese 27 proposed by Huang (2007 ), namely, the interlingual explicitation (i.e. the explicitation of the SL text in the translation) and the comparable or intralingual explicitation (i.e. the explicitation in contrast to the native texts in the TL), which are closely related to the S-universals and T-universals proposed by Chesterman ( 2004 : 39). Given that the corpus-based translation studies in China during the past decade were limited methodologically by corpus construction or theoretically by the TUs hypothesis framework, these studies have immensely improved our understanding of translation universals and their specifi c manifestations in translational Chinese. As Ke ( 2003 : 306) noted, “as a translational phenomenon, explicitation should not only be defi ned in a narrow sense of the change in linguistic cohesion, but should also include the explicitation in meaning transfer, i.e. the additional expressions to help with the reader’s understanding, or the spelling-out of implicit information in the Source Text. This is a translation specifi c phenomenon”. In this sense, Ke’s comment can be taken as a broadened defi nition of the explicitation universal. Along with the explicitation hypothesis, other translation universals such as implicitation, simplifi cation and sanitisation are also discussed by Chinese translation researchers, which will be separately discussed in the following sections.

3.3 Speciﬁ c Research in the Linguistic Features of Translational Chinese

As proposed by Frawley (1984 ), translational language is a “Third Code” which is distinct from either the source language or the target language . Then is translational Chinese a “Third Code”, i.e. different from both ends of translating, particularly from the native writings of Chinese? The majority of the current research on this topic has addressed this question through textual-statistical analyses of a large number of translated Chinese texts in contrast to non-translated or native Chinese texts. These studies, with the common interest of revealing a general picture of translational Chinese, have made some enlightening contributions to the lexical features (e.g. Chen 2006 ; Hu 2006 ; Huang 2007 ; Wang and Qin 2010 ) and syntactical features (e.g. Ke 2003 ; Wang 2003 ; Qin and Wang 2004 ; Hu 2006 ; Wang and Qin 2010 ) of translational Chinese. The following sections will present a detailed review of these ongoing descriptive studies which have been carried out by Chinese translation researchers in the past decade.

3.3.1 The Lexical Features of Translational Chinese

The studies on the lexical features of translational Chinese can be roughly grouped into two categories, the fi rst of which is the “explicitation-hypothesis-oriented” analyses of function word s (e.g. connectives and pronouns) in Chinese translated texts (e.g. Ke 2005 ; Chen 2006 ; Xu and Zhang 2006 ; Hu 2006 ; Huang 2007 ; Wang 28 3 Exploring the Features of Translational Language and Qin 2010 ; Xiao 2010b ). The second category is the investigations of the syntactic components of translated texts (e.g. Wang 2003 ; Qin and Wang 2004 ; Hu 2006 ; Wang and Qin 2010 ). These studies will be introduced as follows in terms of function words and distribution of part-of-speech categories respectively: 1 . The shift of function words The fi rst research of function words in translated Chinese texts is Ke (2005 ) who proposed that explicitation and implicitation coexist in translating, but the degree of explicitation and/or implicitation varies according to the direction of translating between the two languages which have different levels of grammatical explicitation and implicitation. Ke (2005 : 306) elaborated that when translating is done from a “highly grammatical explicit language” (namely, a language which tends to use more function word s systematically to connect sentence components, e.g. English) into a “grammatical implicit language” (i.e. a language which tends to use fewer function words, such as Chinese), the (interlingual) explicitation increases, whereas implicitation decreases. In contrast, if the direction of translating reverses, the translation is expected to have a contrary tendency of grammatical explicitness in the form of function words. Chinese is generally considered to be a grammatical implicit language because of its lack of infl ections or infrequent and non-compulsory use of referential components, intra-sentential and inter-sentential conjunction s in contrast to English and other Indo-European languages which usually have strong and rigid grammatical rules for infl ections, reference markers and conjunctions. Accordingly, when translating from English into Chinese, if the translation is to be accepted as a natural Chinese text, the translator has to reduce the explicitness of the translation and therefore increase its implicitness. As this interlingual increase of explicitness is reasonable and accepted generally as inevitable, the intralingual increase of explicitation in the translated texts in contrast to the non-translated texts in the same TL is, however, hypothetical and debatable among many researchers. Our fi ndings (see Chap. 6 for details) will show that translational Chinese does show this intralingual increase of grammatical explicitness (the use of conjunctions and pronouns in particular). Ke ( 2005 ) used a corpus of 800,000 words of English-Chinese translation to investigate the use of a number of time conjunction s and compared what was found in translational Chinese texts with the result in native Chinese texts, showing that these time conjunctions have a signifi cantly higher frequency in the translated texts than in the non-translated texts, which led to his conclusion that “translational Chinese does show some particularities in its expression” (Ke 2005 : 306). This conclusion was supported by another corpus-based study of the function words in Chinese translated novels (from various source languages) which gives further evidence for the increased frequency of virtually all function word s in translational Chinese (Hu 2005 : 150–160). Based on a 1.8-million-word English-Chinese parallel corpus of popular scientifi c texts, Chen (2004 , 2006 ) also gave supportive evidence for explicitation : conjunctions have higher frequencies in the translated texts of simplifi ed Chinese and traditional Chinese than in the comparable non-translations or native Chinese texts. 3.3 Specifi c Research in the Linguistic Features of Translational Chinese 29

Specifi cally, in contrast to non-translated popular science texts, the translated counterpart tends to spell out the logical relation between clauses and sentences with conjunctions, which in turn increases the grammatical explicitness of the translated texts (Chen 2006 : 352–353). Therefore, Chen concluded that the explicitation of conjunctions can be regarded as a typical characteristic of Chinese translations of popular science. Another similar study is conducted by Huang (2007 ) which compares the use of conditional, adversative and causal conjunction s in the translated in relation to the non-translated Chinese texts based on a bidirectional English-Chinese parallel corpus . The study shows that the frequencies of all the three types of conjunctions in Chinese translations are higher than in native Chinese texts. Besides the more frequent use of conjunction s, Chen ( 2006 ) also presented other linguistic features that may be typical to translated Chinese texts: Firstly, the translated texts show some preference for certain high-frequency conjunctions, termed as the “translational distinctive connectives” (TDCs), which are statistically distinctive from the native Chinese. The convergence of high-frequency conjunctions in the translated texts is also taken as a simplifi cation tendency by some researchers. Secondly, the number of the types of conjunctions in the translated texts shrinks, though some low-frequency conjunctions are found more frequently used in translations. Thirdly, the grammatical function of conjunctions in translated texts appears to be transformed more frequently from inter-sentential connection to intra- sentential connection. Finally, conjunctions in translated text show a stronger tendency to be used separately rather than in pairs. Unlike the previous studies, Huang ( 2007 ) examined the use of personal pronouns in translated and non-translated Chinese texts based on the General English Chinese Parallel Corpus ( GCEPC ) of Beijing Foreign Studies University. He calculated the permillage frequency of personal pronouns in Chinese literary translation (29.02‰) in contrast to that of native Chinese literary texts (16.95‰) and the permillage frequency of personal pronouns in Chinese non-literary translation (1.21‰) in contrast to the non-literary native (0.38‰). Based on these different proportions, and given the representativeness of the GCEPC corpus, it is concluded that translational Chinese, literary or non-literary, has an apparently higher frequency of use of personal pronouns than the non-translational or native Chinese writings. Additionally, the personal pronouns used as the subject of a sentence also turned out to be a preference in Chinese translations from English. Huang went further to infer from his fi ndings that it is perceivable that translational Chinese as the TL of English-Chinese translating is infl uenced by the SL (English) in its increased frequency of personal pronouns. As an important means for anaphoric and cataphoric reference in discourse, the increase of personal pronouns in translational Chinese may be taken as evidence of the “comparable” (i.e. T-universals ) explicitation in translated language (Huang 2007 : 135). A comprehensive investigation to function words in translational Chinese is also reported by Hu (2006 , 2010 ), who focused on Chinese translation of novels from a variety of source languages, taking into account the frequencies of virtually all Chinese function words (auxiliaries, propositions, personal and demonstrative pro- 30 3 Exploring the Features of Translational Language nouns, conjunctions, etc.). His survey came up with the conclusion that almost all these function words have a higher frequency in Chinese translation of novels than in Chinese native novels. 2 . The general lexical features of translational Chinese The general lexical features of translational Chinese are usually discussed by analysing the type/token ratio (TTR), word lists and keyword lists of the respective translated and non-translated corpora as illustrated by the following specifi c research. As is presented in Hu ( 2006 : 136), the standardised type/token ratio ( STTR ), namely, the average type/token ratio counted on the basis of per 1,000 words, of Chinese translated novels is lower than the non-translated Chinese novels, indicating that Chinese translated texts display less variability (i.e. number of different words in the corpus) than native Chinese novels. In the meantime, the lexical density ( LD ) of the translated novels is also lower than the non-translated novels; that is, there are fewer content word s in the translated texts than the non-translated. So, in general, Hu concluded that lexical simplifi cation does present in Chinese translated fi ction. However, a counterexample was given by Wang and Qin ( 2010 ), which suggests, based on a statistical analysis of a subcorpus of the bidirectional GCEPC corpus, that the STTR of (non-literary) translated Chinese is higher than that of the non-translated by 2.3 %. If the representativeness of the corpora and the authors’ statistical methods are unquestionable in the two studies, the confl icting evidence may be taken as a need for the reconsideration of the simplifi cation hypothesis because different genres and registers (literary or non-literary) tend to have different choices in terms of the words actually used. The word list of a corpus is a list of the word types in the corpus sorted in ascending or descending order according to the frequencies or alphabetically. Hu ( 2006 ) gave a detailed analysis of the word lists of translated and non-translated novels in Chinese, according to which the sum total of the frequencies of the top 30 types in Chinese translated novels takes up 36.66 % of whole corpus, much higher than that of the non-translated counterpart; meanwhile, there are 6 high-frequency personal pronoun s in the list head of the translated corpus, whereas the non-translated corpus has only 4 high-frequency personal pronouns (Hu 2006 : 141). This was regarded as evidence for the lexical simplifi cation in translated fi ction, or more specifi cally, it seems the Chinese translated novels tend to use high-frequency words more frequently than non-translated novels. In addition, Hu (2006 : 142–149) used the preference of high-frequency personal pronouns in Chinese translated texts as an explanation for the reduction of nouns in translational language. The keyword list is different from the word list in that it is a list of the word types that are used with an unusually higher frequency when the corpus is compared with a reference corpus. The analyses of the keyword list in part-of-speech annotated corpora show that the translated Chinese corpus has some particularity in its use of verbs, adjectives and adverbs (Wang and Qin 2010 ). Another feature worthy of mentioning is that some morphemes in the translated Chinese corpus seem to have a stronger capability for word formation, for example, the Chinese suffi x “-ᙗ” xing 3.3 Specifi c Research in the Linguistic Features of Translational Chinese 31

(gross translation: quality or property) is observed to have an increased formational ability in the translated texts, i.e. the compound words formed by this sufﬁ x have a greater number of occurrences in translation, for example, ⤜ࡋᙗ duchuang-xing ,( original ity ), ߣᇊᙗ jueding-xing (decidability ), ਟؑᙗ kexin-xing (creditab ility) ᇎ䍘ᙗ shizhi-xing (essentiality ), etc. Wang and Qin (2010 ) suggested that the increased formational capability of -xing is connected to the English sufﬁ x -lity used in the source text, therefore providing evidence of SL interference from English to Chinese.

3.3.2 The Syntactical Features of Translational Chinese

1 . Sentence pairs and average sentence length in English-Chinese translation Wang (2003 ) investigated the relationship of sentence pairs in translations between English and Chinese on the basis of the GCEPC parallel corpus . The empirical data retrieved from the corpus allowed the researcher to make a summary of the sentence pairs in translations between the two languages: The proportions of correspondent sentence pairs in either Chinese-English or English- Chinese translations range from 60 % to 90 %, with the matchability fl uctuating across the directions of translating and registers (literary or non-literary). In general, in literary translation from Chinese to English, the proportion of one-to-one match (that is one sentence in the ST matches one sentence in the TT) stays between 54–82 %, averaging 63.3 %; in non- literary translation from Chinese to English the ratio of one-to-one match rises from 64 to 91 %, averaging 80.2 %. The other way around, English-Chinese literary translation has a one-to-one matchability between 70–97 %, with average of 81.9 %; whereas the non- literary English-Chinese translation shows a one-to-one ratio of 71–94 %, the average being 84.7 %. There is an obvious ascending curve in these matchability ratios: in terms of translating direction, English-Chinese translation has a higher matchability of one-to-one sentences; in terms of genres, non-literary translation has a higher ratio of matchable one-to-one sentences. (Wang 2003 : 114) As such, the correspondent proportion between ST and TT sentences varies signifi cantly across registers. The differences could be bigger if we look at the specifi c texts. However, Wang ( 2003 : 114) concluded that the main stream of matchable sentence pairs in English-Chinese mutual translation is the one-to-one pattern. Through the analyses of this pattern in great details, Wang was able to specify these relationships: in Chinese-English translating, sentences in Chinese source texts are on average longer than the sentences in English target texts; as a result, it is necessary to break and reconstruct the Chinese sentence in English translation; and accordingly, the English translation is relatively less infl uenced by the Chinese syntactical structure. However, in English-Chinese translating, the Chinese translation is more infl uenced by the English syntax and punctuation so that the one-to-one matchable pattern becomes predominant (Wang 2003 : 414). Moreover, the sentence paring between ST and TT is not only subject to the direction of translating or the SL syntax but also subject to the translator’s individual decisions and stylistic preference in translating. Wang ( 2003 ) gave as an example that it is not rare in English- 32 3 Exploring the Features of Translational Language

Chinese translating that no full stops are used at the end of many sentences, which may be one of reasons why the translated Chinese texts have a longer average sentence length than the native Chinese texts. In addition, Wang and Qin’s (2010 ) statistical results of sentence length and clause length indicate that sentences in translated Chinese texts are longer than native Chinese sentences by an average of 2.46 words. The average sentence length (ASL) of English source texts is 18.23 words, in contrast to the ASL of native Chinese texts of 25.81 words, while the ASL of translated Chinese texts reaches 28.27 words. Wang and Qin explain the reasons why sentences of translated Chinese texts are longer than those of English source texts in terms of linguistic typology: as a typical isolating (analytic) language, Chinese usually resorts to lexical means in order to express corresponding meanings usually expressed by infl ections in a synthetic language like English. For example, translating the relative pronoun “that” will possibly end up with a longer Chinese translation, because there is no correspondent word for “that” in Chinese and the translator has to add more words to clarify the relation. Wang and Qin’s conclusion of longer sentences in Chinese translation than both English source texts and native Chinese texts is not immedi- ately supported by Chinese native speakers’ reading experience, because we usually think English sentences are longer than their Chinese counterparts. However, the authors suggest that although sentences in Chinese translation are statistically longer, the diffi culty of reading longer sentences is released by Chinese punctuation of commas that helps to shorten the clauses within a sentence. The above empirical observation is also supported by other studies. For example, Chan ( 2007 : 38–39) reached a similar conclusion for the sentence length of translated legal texts of Hong Kong. Hu (2006 : 167) also produced the same result that the sentences and paragraphs of Chinese translated novels are longer than native Chinese novels. Hu also compared the ASL of Chinese translated novels with that of the Brown corpus , giving the statistical result that sentences of Chinese translated fi ction are longer than either native Chinese fi ction or native English novels. 2 . The general syntactical features of translational Chinese In addition to the macro-level observation and explanation of sentence length in translated texts, the syntactical analysis of translational Chinese also extends to some particular Chinese syntactic constructions with the aim of revealing the typical syntactic features of translational Chinese. For example, Ke ( 2003 ) undertook an investigation into the “ba ( ᢺ) construction”, a structure with a long history of concern among Chinese linguists. Based on a large-sized parallel corpus, Ke compared the distribution of the ba constructions in both native and translated Chinese texts, which revealed that translated texts have a higher frequency of ba constructions in contrast to the native texts in literary or non-literary registers. Besides higher frequency, he also found the grammatical restriction upon the object after the leading preposition ba seems less rigid in the translated than non-translated. As the ba construction is particular in Chinese, Ke ( 2003 ) asserted the higher number of ba constructions is 3.3 Specifi c Research in the Linguistic Features of Translational Chinese 33 obviously not from the English SL. Another study by Hu and Zhu (2008 ) on ba constructions in Chinese translations of Hamlet rendered support to Ke (2003 ), which also found there are more occurrences of ba constructions in the translated than non- translated Chinese literary texts. They attributed the more frequent use of ba construction to the individual strategies adopted by the translator. The structural auxiliary - de ( Ⲵ) is the most frequently and widely used word token in modern Chinese, whereas the excessive use of -de in many native and translated Chinese writings has been criticised by some Chinese scholars and writers (Yu 2002 ). In corpus-based translation studies, Hu (2006 : 180–191) offered a detailed study of - de constructions. He pointed out, in addition to increased frequency, -de constructions in translated Chinese novels are longer as well as structurally more complex than in the non-translated novels. In his examination of the passives in the corpus of Chinese translated fi ction, Hu found the frequency of bei passive s is close to that of the non-translated, which is considered to be a normalisation tendency (Hu 2006 : 196). Further investigation showed long passive s (i.e. the passives with a patient agent) in Chinese translated novels are less frequent in the translated than in the native novels; this feature is interpreted as a tendency to conventionalise the translation or, in other words, to enhance the acceptability of the translated text. However, as we will see in Chap. 7 of this book, the passives used in literary or non-literary registers tend to have signifi cant differences. Apart from the studies of particular syntactic structures in Chinese and their presentation in translated texts, researchers have also endeavoured to identify the sources of the variation in Chinese translation in terms of the SL interference of English. For example, Wang (2004 ) looked into the English structure “so…that” and its correspondent Chinese structure “ruci…yizhi… ” (“ྲ↔…ԕ㠤…”) on the basis of a bidirectional English-Chinese and Chinese-English parallel corpus. The fi ndings of the research are interesting: as a frequently used common structure, “so…that” has many equivalent expressions in translated Chinese texts, whereas there are barely any correspondent instances found in native Chinese writings (Wang 2004 : 41–46). From this study, it may be inferred that it seems possible to introduce into the TL new syntactic structure through translating or to increase the frequency of some less frequent structures (Qin and Wang 2004 : 47). More interestingly, based on parallel corpus examinations, Wang and Qin ( 2010 ) also found that some expressions which may have come into use through the translating process have established themselves for some reason in the Chinese linguistic system, and some of them have even taken the place of the old forms over time. For example, the modern Chinese expression “suizhe shijian-de tuiyi ” (䲿⵰ᰦ䰤Ⲵ᧘〫, gross translation: as time goes by), which was thought to have come from translating, has already shown the tendency of replacing the old Chinese four-character set phrases, like “guangyin renran ” (ݹ䱤㥿㤂, “light shadow goes by”), “ri fu yi ri ” (ᰕ༽аᰕ, “day again one day”) and “suiyue liushi ” (኱ᴸ⍱䙍, “years months fl ow on”). 34 3 Exploring the Features of Translational Language

3.4 Problems in the Current Research

It can be seen from the above review of the recent studies carried out by Chinese corpus-based translation researchers that the successful creation of a number of parallel and comparable translation corpora, reusable, large-sized (millions of words) and easily accessible to corpus and translation theorists, has laid an important foundation for corpus-based translation studies in Chinese. These studies involve many linguistic features of translational language (translational Chinese in this book), ranging from lexical to syntactical profi les, some of which are inspir- ingly explained within the translation universals hypotheses among others. Given all these empirical and theoretical contributions of those studies to translation studies and contrastive language studies in general, there are some problems perceivable as it seems to us in the previous research. We will summarise those problems as a basis for further discussion and research in this area. As we see from the studies reviewed in the fi rst section of this chapter, the research of translational English and its close relatives, a number of other European languages did bring forth some interesting fi ndings, but unfortunately these fi ndings from various case studies often produce confl icting results or even sometimes utterly different understandings of same linguistic feature (e.g. average sentence length was taken as an indication of explicitation by some researchers but was also understood as a symbol of syntactic simplifi cation by others.) More importantly, as we mentioned in Chap. 1 , there remains a very challenging question for the supporters of translation universals hypotheses : Is it well-grounded to generalise the universals of translation from the translated texts among a small number of closely related languages? How about the genetically unrelated languages like Chinese and English which belong to different language families? As the research reviewed in this chapter has shown, there are indeed rapid developments in the research of translational Chinese in the past 15 years. However ice breaking and illuminating these developments, there are still some drawbacks in regard to some fundamental issues in both theory and methodology. Firstly, it is obvious that most of the studies, although wide ranging across many linguistic properties of translational Chinese, are based on a very limited number of corpora which are not unquestionable in terms of the corpus design and representativeness of the texts. For example, the largest bidirectional parallel translation corpus in China, the GCEPC created by Kefei Wang since 2004, has reached the size of 20 million words, including comparable and parallel translated texts in English and Chinese. Although the GCEPC has embraced two general genres of texts, i.e. literary and non-literary texts, the literary subcorpus has taken an overwhelmingly large proportion of the whole corpus, whereas the “encyclopaedic” subcorpus is by no means balanced in text genres nor subjects. Meanwhile, the sampling period of the texts covers such a long time span as to bring in the factor of language change undesirable for the synchronic contrastive analysis of translation universals . Other corpora used by Chinese translation theorists are either humble in size or constrained by the text genres included. Chen ( 2006 ) is a PhD dissertation based on a 3.4 Problems in the Current Research 35 parallel English-Chinese corpus of popular science texts, but obviously the subject area of popular science only prevented his conclusions from being extended easily to translational Chinese as a whole. Hu (2006 ) built a monolingual translation corpus, the Contemporary Chinese Translated Fiction Corpus . The corpus is well sampled in order to ensure the representativeness of the corpus for contemporary translated novels in Chinese; however it is not a balanced corpus, which, similarly, makes it diffi cult to generalise the fi ndings. Given the great empirical signifi cance of these corpora, most of them were not designed to be comparable to other translation corpora in other languages, which is necessary for the higher-level generalisation of TUs . Secondly, the studies of translational Chinese as reviewed in this chapter in general lack a systematic theoretical framework that is required for the width and depth of all the case studies. The majority of these case studies merely supply evidence or counter evidence for the translation universals hypotheses proposed by western researchers. Most of the concerns of the typical features of translated Chinese texts only touched upon a few superfi cial formal features like connectives and pronouns. The syntactic analysis of translated texts has just begun. As such, in order to further nurture the research of translational Chinese, there are at least two approaches: on the one hand, it is necessary to improve the quality of translation corpora we use in research by taking more rigid sampling criteria for corpus texts and taking into consideration the comparability of the translation corpus with the classic native language corpora already in existence; on the other hand, it is necessary to search for new methodologies and breakthroughs for a comprehensive and systematic description of translational Chinese. This chapter reviews the studies of translation universals in European countries and the empirical studies of translational Chinese in China. As shown by these studies, the interest in translation universals or the typical features of translational language has become a promising research hotspot, but in the meantime, this interest is being hindered by some methodological and theoretical problems. This book is an attempt to solve these problems: a balanced corpus of translational Chinese, with rigid sampling requirements, will be built as the matchable database for the correspondent corpus of native Chinese; and an integrated theoretical framework will embrace both translational Chinese as a whole and the specifi c differences across registers. Chapter 4 Corpora and Corpus Tools in Use

It is a well-recognised fact that the appropriateness of the corpora used in any corpus-based language studies is dependent on the research questions that the researcher is to answer. For example, if it is the systematic differences between British English and American English that one is interested in, the appropriate corpora should be written and spoken corpora representative of both varieties of English. But if it is the changes and evolvement of English during the past centuries that the researcher is studying, a diachronic corpus of English that is representative to the development of English in different historic periods is clearly required. As such, the representativeness of the corpora for use is key to corpus-based language research. As the old saying goes, “rubbish in, rubbish out”, the corpora we use is essential to the reliability of our study. The partiality of a corpus will be unconvinc- ing for a study that is supposed to produce general conclusions. In other words, there are no “good” or “bad” corpora in the absolute sense. The key factor of the appropriateness of a corpus is whether it is suitable to be used to answer specifi c research questions. But unfortunately, the creation of a well- designed representative corpus is not only a time-consuming but also a pricey matter. Accordingly, there is consensus among corpus researchers that the most effi cient way of corpus use is to make appropriate use of the publicly available corpora already in existence before starting new projects of corpus construction or until the expense of getting access to available corpora exceeds the expense of building a new one. As introduced in Chap. 2 , this book is a corpus-based study of translational Chinese, which makes it necessary to introduce the corpora used in the research: fi rst, the Chinese reference corpora and, second, the two comparable corpora of native and translational Chinese, namely, the Lancaster Corpus of Mandarin Chinese ( LCMC ) and the Zhejiang University Corpus of Translational Chinese (ZCTC ). In the second half of this chapter, we will also introduce the corpus analytical tools that we resort to during the research.

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 37 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_4 38 4 Corpora and Corpus Tools in Use

4.1 Current General Corpora of Chinese in Use

With the rapid development of Corpus Linguistics since the 1950s, many Chinese corpora have appeared on the stage. Some of them can be used as reference corpora or as technical standards for future corpus construction. We will introduce in this section a few most representative general or balanced corpora of written Chinese to give the reader an overview of Chinese corpus construction and corpus-based research. The fi rst annotated corpus of modern Chinese, the Academia Sinica Balanced Corpus of Modern Chinese , simplifi ed as the Sinica Corpus, was built in the mid- 1990s. The Sinica Corpus is a representative corpus of Mandarin Chinese as used in Taiwan. The current version (5.0) of the corpus contains 10 million words of texts sampled from different areas and classifi ed according to fi ve criteria: (1) genre (including press reportage, press review, advert, letter, etc.), (2) style (narrative texts, argumentative texts, expository texts and descriptive texts), (3) mode (written, written to be read, written to be spoken, spoken and spoken to be read), (4) topics (philosophy, natural science, social science, arts, general/leisure and literature) and (5) source (newspaper, general magazine, academic journal, textbook, reference book, thesis, general book, audio/video medium, conversation/interview and public speech). The values of these parameters, together with bibliographic information, are encoded at the beginning of each text in the corpus. The whole corpus is annotated with part-of-speech tags and a range of linguistic features such as nominalisa- tion and reduplication. The Sinica Corpus is publicly available online; however, due to the historical reasons, Taiwan has been isolated from the mainland for over half a century, and the Chinese used in Taiwan is noticeably different from the same language used in the mainland. As such, the Sinica Corpus cannot be taken as representative of Chinese in general, particularly non-representing the mainland Chinese (Xiao et al. 2004 ). The Modern Chinese Language Corpus (MCLC) is China’s national corpus built under the auspices of the National Language Committee of China. The corpus contains 100 million Chinese characters of systematically sampled texts produced during 1919–2002, with the majority of texts produced after 1977. The corpus covers three large categories (humanities/social sciences, natural sciences and miscellaneous text categories such as offi cial documents, ceremony speech and ephemera) including more than 40 subcategories. Text categories containing over fi ve million characters include literature, society, economics, newspapers, miscellaneous and legal texts, with literary texts accounting for the largest proportion (nearly 30 million characters). Most samples in the corpus are approximately 2,000 characters in length, with the exception of samples taken from books, which may contain up to 10,000 characters. The digitalised texts were proofread three times so that errors are less than 0.02 % (see Wang 2001: 283). All text samples in the MCLC corpus are encoded with detailed bibliographic information (up to 24 items) in the corpus header. A core component of the corpus, which is composed of 50 million Chinese characters, has been tokenised (with an error rate of 0.5 ‰) and POS tagged (with 4.1 Current General Corpora of Chinese in Use 39 an error rate of 0.5 %), while a small part of it (one million characters, in 50,000 sentences) has been built into a treebank. Presently, a scaled-down version of the corpus, which contains 20 million characters proportionally sampled from the larger corpus, has been made available to the public free of charge for online access at the MCLC website. The Centre for Chinese Linguistics at Peking University has maintained a corpus of both modern and ancient Chinese. The CCL corpus contains a 307-million- Chinese-character subset of modern Chinese texts ranging across many genres from newspaper, magazine and literature to practical writings. With a total inclusion of 477 million Chinese characters, the CCL corpus is the largest online Chinese corpus available publicly, but the corpus is not sampled in a balanced way across text genres, and a majority of the texts included are literary texts. Moreover, the online corpus contains texts that are not segmented and annotated with part-of-speech tags. The LIVAC corpus of Chinese is a synchronous Chinese corpus which was launched in 1995 by the Language Information Sciences Research Centre of the City University of Hong Kong and was later hosted by the Hong Kong Institute of Education’s Research Centre on Linguistics and Language Information Sciences and is now maintained by Chilin (HK) Ltd. The LIVAC (Linguistic Variation in Chinese Speech Communities) project started in 1993 with the aim of building a synchronous corpus for studying varieties of Mandarin Chinese. For this purpose, data has been collected regularly and simultaneously, once every 4 days since July 1995, from representative Mandarin Chinese newspapers and the electronic media of six Chinese-speaking communities: Hong Kong, Taiwan, Beijing, Shanghai, Macau and Singapore. The contents of these texts typically include the editorial, and all the articles on the front page, international and local news pages, as well as features and reviews. The corpus is planned to cover a 10-year period between July 1995 and June 2005, capturing salient pre- and post-millennium evolving cultural and social fabrics of the diverse Chinese speech communities (Tsou et al. 2000). The collection of materials from these diverse communities is synchronised with uniform calendar reference points so that all of the components are comparable. The LIVAC corpus contains over 150 million Chinese characters, with 720,000 word types in its lexicon. All of the corpus texts in LIVAC are segmented automatically and checked by hand. In addition to the corpus, a lexical database is derived from the segmented texts, which includes, apart from ordinary words, those expressing new concepts or undergoing sense shifts, as well as region specifi c words from the six communities. The database is thus a rich resource for research into linguistics, sociolinguistics and Chinese language and society. As LIVAC captures the social, cultural and linguistic developments of the six Chinese-speaking communities within a decade, it allows for a wide range of comparative studies on linguistic variation in Mandarin Chinese. The corpus also provides an important resource for tracking lexical development such as the evolution of new concepts and their expressions in present-day Chinese. While the access to the entire corpus is restricted to registered users only, a sample (covering the period from 1 July 1995 to 30 June 1997) can be searched using the online query system at the LIVAC site, which shows KWIC concordances as well as frequency distributions across the six speech 40 4 Corpora and Corpus Tools in Use communities. One defect of the LIVAC corpus is that it only contains media texts; meanwhile it is only available for research projects based within constructing and maintaining institutions. Obviously, the above introduction does not include specialised corpora of Chinese such as spoken corpora, newspaper corpora, among others, nor does it include the Chinese corpora that were used by us in this book, which will be discussed in detail in the next section. The brief review of some of the most representative general Chinese corpora is to reveal the fact that the few well-known general Chinese corpora available are not suitable for our research purpose. These general corpora, being large in size and relatively well designed and constructed, are only for the sake of research into Chinese as a native tongue, none of which contains translated texts as required by our study. Another problem is, although some of these corpora are available to search online, the users are limited by the copyright issue to get access to corpus texts themselves in reality. The online concordancer can only offer the most basic search functions, which further hinders the more sophisticated linguistic analysis of the texts. As a result, it turns out the creation of the fi rst balanced corpus of translational Chinese is defi nitely necessary. The following section will give a detailed demonstration of the two corpora used by us, the Lancaster Corpus of Mandarin Chinese (LCMC) and it translational counterpart, the Zhejiang University Corpus of Translational Chinese ( ZCTC ).

4.2 The Lancaster Corpus of Mandarin Chinese

The Lancaster Corpus of Mandarin Chinese (LCMC) is a one-million-word balanced corpus of written Mandarin Chinese. The corpus was created by Tony McEnery and Richard Xiao of Lancaster University as part of the research project “Contrasting tense and aspect in English and Chinese” funded by the UK Economic and Social Research Council (ESRC). The LCMC corpus was designed as a Chinese match for the FLOB (see Hundt et al. 1998 ) with the aim of creating a publicly available balanced corpora of Chinese for corpus-based synchronic studies of Chinese and the contrastive studies of Chinese and English (McEnery and Xiao 2004). This section will discuss such related issues concerning the creation of LCMC as for the corpus design, sampling, markup, encoding, segmentation and annotation of the corpus.

4.2.1 The Brown Corpus or LOB Model

The ﬁ rst modern corpus of English, the Brown University Standard Corpus of Present-Day American English (i.e. the Brown corpus ; see Kucĕra and Francis 1967 ), was built in the early 1960s for written American English. The population from which samples for this pioneering corpus were drawn was written English texts published in the USA in 1961, while its sampling frame was a list of the 4.2 The Lancaster Corpus of Mandarin Chinese 41 collection of books and periodicals in the Brown University Library and the Providence Athenaeum. The target population was ﬁ rst grouped into 15 text categories, from which 500 samples of approximately 2,000 words were then drawn proportionally from each text category, totalling roughly one million words. The Brown corpus was constructed with comparative studies in mind, in the hope of setting the standard for the preparation and presentation of further bodies of data in English or in other languages. This expectation has now been realised. Admittedly, with the booming of computer and IT technologies, corpora in recent years have become unprecedentedly large in size, for example, the 100- million-word British National Corpus (BNC) and ever-growing monitor corpus, Bank of English , which has already reached the size of 650 million words; however, the corpus construction frame launched by the Brown corpus was not washed away by time; instead, it has become a “classic” in modern corpus linguistics . Since its completion, the Brown corpus model has been followed in the construction of a number of corpora for synchronic and diachronic studies as well as for cross-linguistic contrast. For example, the LOB (the Lancaster -Oslo/Bergen corpus of British English; see Johansson et al. 1978 ) was built to be a counterpart for the Brown corpus in order to compare American and British English as used in the early 1960s. The updated versions of the two corpora, Frown (see Hundt et al. 1999 ) and FLOB (see Hundt et al. 1998 ), can be used to compare the two major varieties of English as used in the early 1990s. Other corpora of a similar sampling period, such as ACE (the Australian Corpus of English , also known as the Macquarie corpus), WWC (the Wellington Corpus of Written New Zealand English) and Kolhapur (the Kolhapur Corpus of Indian English ), together with FLOB and Frown , allow for comparison of “world Englishes”. In fact, the International Corpus of English (ICE), which is designed and constructed particularly for research into the English varieties of different regions, is also based on the Brown corpus model, but with updated text categories and text distribution ratios. There are also corpora built for diachronic studies, which took the Brown corpus model. Besides the Brown versus Frown , the Lancaster 1931 (see Leech and Smith 2005), LOB and FLOB all provide a reliable basis for tracking recent language change over 30-year periods. Lancaster University has recently launched a number of new corpora of the Brown family, for example, the Lancaster-1901 and the Lancaster-1931, which respectively represent the English texts in the beginning of the twentieth century and the 1930s (Leech 2011). Other corpora, such as the LCMC corpus (see McEnery et al. 2003 ) and the UCLA Written Chinese Corpus (Tao and Xiao 2007), when used in combination with the FLOB / Frown corpora, provide a valuable resource for contrastive studies between Chinese and two major varieties of English.

4.2.2 The Sampling Frame and Text Collection

As the LCMC corpus was originally created for use on the research project “Contrasting tense and aspect in English and Chinese”, the corpus builders ﬁ rst needed to make a decision regarding which English corpus we should use for 42 4 Corpora and Corpus Tools in Use

Table 4.1 LCMC text category Code Text category Samples Proportion (%) A Press reportage 44 8.8 B Press editorials 27 5.4 C Press reviews 17 3.4 D Religion 17 3.4 E Skills/trades/hobbies 38 7.6 F Popular lore 44 8.8 G Biographies/essays 77 15.4 H Miscellaneous 30 6 J Science 80 16 K General fi ction 29 5.8 L Mystery/detective fi ction 24 4.8 M Science fi ction 6 1.2 N Western/adventure fi ction 29 5.8 P Romantic fi ction 29 5.8 R Humour 9 1.8 Total 500 100 contrastive purposes so that we could follow its sampling frame. After reviewing the available English corpora, we decided to create a match for FLOB , a balanced corpus of British English, as FLOB sampled from a period in which electronic Chinese texts were produced in reasonable quantity (1991–1992). Also, FLOB, at one million words, was large enough to be useful, yet small enough to be able to build a Chinese match with relative ease. A further attraction of FLOB is that it has a matching American English corpus, Frown . Hence, by building a match for FLOB, we enabled a contrast of Chinese with the two major varieties of English. The fi nal decision was made to follow the sampling frame of FLOB, which contains fi ve hundred 2,000-word samples of written British texts sampled from 15 text categories in 1991–1992, totalling one million words (Table 4.1 ). In LCMC, the FLOB sampling frame is followed strictly except for two minor variations. The fi rst variation relates to the sampling frame—the western and adventure fi c- tion (category N) is replaced with martial arts fi ction. There are three reasons for this decision. First, there is virtually no western fi ction written in Chinese for a mainland Chinese audience. Second, martial arts fi ction is broadly a type of adventure fi ction and as such can reasonably be viewed as category N material. It is also a very popular and important fi ction type in China and hence should be represented. Finally, the language used in martial arts fi ction is a distinctive language type, and hence, given the wide distribution of martial arts fi ction in China, once more one would wish to sample it. The language of the martial arts fi ction texts is distinctive in that even though these texts were published recently, they are written in a form of vernacular Chinese, i.e. modern Chinese styled to appear like classical Chinese. Although the inclusion of this text type has made the tasks of part-of-speech (POS) tagging and the post-editing of the corpus more diffi cult, the inclusion of the texts 4.2 The Lancaster Corpus of Mandarin Chinese 43 has also made it possible for researchers to compare representations of vernacular Chinese and modern Chinese. The second variation in the sampling frame adopted from FLOB was caused by problems we encountered while trying to keep to the FLOB sampling period. Because of the poor availability of Chinese electronic texts in some categories (nota- bly F, D, E and R) for 1991, we were forced to modify the FLOB sampling period slightly by including some samples ±2 years of 1991 when there were not enough samples readily available for 1991. As can be seen from Table 4.2 , most of the texts were produced ±1 year of 1991. We assume that varying the sampling frame in this way will not infl uence the language represented in the corpus signifi cantly. LCMC has been constructed using written Mandarin Chinese texts published in mainland China to ensure some degree of textual homogeneity. It should be noted that the corpus is composed of written textual data only, with items such as graphics and tables in the original texts replaced by elements in the corpus texts. Long citations from translated texts or texts produced outside the sampling period were also replaced by elements so that the effect of translationese could be excluded and L1 quality guaranteed. Although a small number of samples, if they were conformant with our sampling frame, were collected from the Internet, most samples were provided by the SSReader Digital Library in China. As each page of the electronic books in the library comes in PDG format, these pages were transformed into text fi les using an OCR module provided by the digital library. This scanning process resulted in a 1–3 % error rate, depending on the quality of the picture fi les. Each electronic text fi le was proofread and corrected independently by two native speakers of Mandarin Chinese so as to keep the electronic texts as faithful to the original as possible. The digital library has a very large collection of books; it, however, does not provide complete newspapers but provides texts from newspapers or newswire stories instead. News texts in the library are grouped into a dozen collections of news arranged to refl ect broad differences of text types (e.g. newswire versus newspaper articles) or medium (e.g. newspaper texts versus broadcast news scripts). These collections, however, represent news texts from more than 80 newspapers and television or broadcasting stations. The samples from these sources account for around two-thirds of the texts for the press categories (A–C) in LCMC. The other third was sampled from newswire texts from the Xinhua News Agency. Considering that this is the most important and representative news provider in China, roughly analogous to the Associated Press in the USA/UK, we believe that the high proportion of material taken from the Xinhua News Agency is justifi ed (Table 4.2 ). Unlike languages such as English, in which words are typically delimited by white space and thus word counts can be produced in written texts relatively easily, Chinese contains running characters. Consequently, whereas it is easy to count the number of characters in a text, it is much more diffi cult to count the number of words. The diffi culty in word counting in Chinese posed a challenge for us, as we wanted to extract roughly 2,000-word chunks from larger texts. Rather than count the words by hand, which would have proved time consuming, we proceeded by estimating a character to word ratio. Based on a pilot study carried out by us, we decided to adopt a ratio of 1:1.6, which meant that we needed a 3,200-character 44 4 Corpora and Corpus Tools in Use

Table 4.2 Sampling period of LCMC (all values are percentages) Code 1989 1990 1991 1992 1993 A – 22.7 72.7 2.3 2.3 B 7.4 14.8 51.9 3.70 22.2 C – 5.9 88.2 5.9 – D 5.9 17.6 41.2 11.8 23.5 E – 23.7 44.7 10.5 21.1 F 6.8 25 29.5 13.6 25 G 1.3 10.4 64.9 16.9 6.5 H – – 100 – – J 1.2 7.5 72.5 17.5 1.3 K – – 79.3 13.8 6.9 L – 8.3 62.5 16.7 12.5 M – – 100 – – N 3.4 13.8 48.3 31.1 3.4 P 10.3 6.9 55.2 20.7 6.9 R – – 44.4 22.2 33.3 running text to gather a 2,000-word sample. When a text was less than the required length, texts of similar quality were combined into one sample. For longer samples, e.g. those from books, we adopted a random procedure so that beginning, middle and ending samples of texts were included in all categories. It should be noted that in selecting chunks we operated a bias in favour of textually coherent chunks that ﬁ tted our sampling size, e.g. we favoured samples that did not split paragraphs over those that did. Although the character to word ratio we adopted worked on most text types, it also resulted in some samples of slightly more than 2,000 words and some of slightly fewer than 2,000 words. This was typically the case where texts contained a large number of proper nouns or idioms, some of which are four-character or even seven-character words. Consequently, when these samples were processed and it was possible to count the number of words easily, we were forced to adjust the size of each sample that was ﬁ nally included in the corpus. The adjustment was done by cutting longer samples to roughly 2,000 words while avoiding truncating the last sentence or reducing the whole sample to fewer than 2,000 words. Nonetheless, although some individual samples still contain fewer and a few more words than 2,000, the total number of words for each text type is roughly conformant to our sampling frame.

4.2.3 Encoding and Markup

Unlike writing systems typically encoded by single bytes, such as the Roman alpha- bet, the Chinese writing system typically requires two bytes. Currently there are three dominant encoding systems for Chinese characters: GB2312 for simplifi ed Chinese, Big5 for traditional Chinese and Unicode. Both GB2312 and Big5 are 4.2 The Lancaster Corpus of Mandarin Chinese 45 double-byte encoding systems. Although the original corpus texts were encoded in GB2312, we decided to convert the encoding to Unicode (UTF-8) for the following reasons: (1) to ensure the compatibility of a non-Chinese operating system and Chinese characters and (2) to take advantage of the latest Unicode-compliant concordancers such as Xara (Burnard and Todd 2003) and WordSmith Tools version 5.0. And because WordSmith Tools 5.0 only works for UTF-16, we have also pre- pared a WordSmith version of LCMC (see Appendix 3 ). To make it more convenient for users of our corpus with an operating system earlier than Windows 2000 and no language support pack to use our data, we have produced a Romanised Pinyin version of the LCMC corpus in addition to the standard version containing Chinese characters. Although also encoded using UTF-8, the Pinyin version is more compatible with older operating and concordance systems. This is also of assistance to users who can read Romanised Chinese but not Chinese characters. Both versions of the corpus are composed of 15 text categories. Each category is stored as a single fi le. The corpus is XML conformant. Each fi le has two parts: a corpus header and the text itself. Conformant to the European Language Resources Association Metadata Schemes (version 1.4), the header fi le contains general information about the corpus in XML format. As shown in Fig. 4.1 , these information include identifi cation factors (such as the full and short name of the corpus, reference, language, sampling period, version and version history), content (corpus size, encoding and markup, text source, annotation), production (research project and the year of production), application (current and potential application, areas of application), technical information (storage space, development approach), validation, additional material, distribution, etc. Figure 4.2 shows an excerpted sample of an annotated paragraph chosen from the text category H (miscellaneous). The text part is annotated with fi ve main features, as shown in Table 4.3 : (1) text category, (2) fi le identifi er, (3) paragraph, (4) sentence and (5) word,

Fig. 4.1 An example of the corpus header of LCMC 46 4 Corpora and Corpus Tools in Use

Fig. 4.2 A sample of an annotated paragraph in LCMC

Table 4.3 XML elements of text Level Code Gloss Attribute Value 1 text Text type TYPE As per Table 4.1 Text category ID As per Table 4.1 Code 2 fi le Corpus fi le ID Text ID plus individual fi le Number starting from 01 3 p Paragraph – – 4 s Sentence n Starting from 0001 onwards 5 w Word POS Part-of-speech tags as per the LCMC tagset c Punctuation and symbol POS As per the LCMC tagset gap Omission – –

punctuation/symbol and elements omitted in transcriptions. These details are useful when using an XML-aware concordancer such as Xara version 1.0. With this tool, users can either search the whole corpus or defi ne a subcorpus containing a certain text type or a specifi c fi le. The POS tags allow users to search for a certain class of words and, in combination with tokens, to extract a specifi c word that belongs to a certain class. 4.2 The Lancaster Corpus of Mandarin Chinese 47

4.2.4 Segmentation and POS Annotation

We undertook two forms of corpus annotation on the LCMC corpus: word segmentation and part-of-speech annotation. To deal with each of these in turn, word segmentation is an essential and non-trivial process in Chinese corpus linguistics (see Wu and Fung 1994 ; Sun et al. 1998 ; Swen and Yu 1999 ). Segmentation, or tokenisation, refers to the process of segmenting text strings into word tokens, i.e. deﬁ ning words (as opposed to characters) in a running text. For alphabetic languages, as word tokens are generally delimited clearly by a preceding white space and a following white space or a new-line character, “the one-to-one correspondence between an orthographic token and a morphosyntactic token can be considered the default case that applies in the absence of special conditions” (Leech 1997 : 21–4). However, for Chinese (and some other Asian languages such as Japanese and Thai), word segmentation is not a trivial task, for, as noted already, a Chinese sentence is written as an unseparated string of characters. And technically speaking, a Chinese text can only be processed by corpus tools such as WordSmith after it being segmented or tokenised in words. Readers unfamiliar with Asian languages such as Chinese may think it strange that segmentation is such a vital process in Chinese corpus linguistics. Yet segmentation in Chinese corpus linguistics is vital for at least two reasons. First, although a rough character to word correspondence in Chinese does at times exist, it is not possible to simply search for a character and assume it is a word or always part of one word. Some characters in Chinese are meaningless. For example,⩥ pi is meaningful only when it goes with pa to form a word, namely,⩥⩦ pipa (a musical instru- ment). Second, the main purpose of segmentation is disambiguation. Consider the following sentence:

ԆԜ нᗇн 䗷 ањ ⚠㢢 Ⲵ ൓䈎㢲Ǆ Tamen budebu guo yi-CL huise-de shengdanjie They had to spend a grey Christmas.

The underlined part, “bu de bu”, when taken in isolation, can be segmented and understood in different ways: (1) (must not) (not) (spend), (2) (must not) (but, only) or (3) (have to) (spend), although in this example only (3) is meaningful in this sentence. Literate speakers of Chinese do not have any diffi culty interpreting this sentence in its written form, precisely because they are actually segmenting the character string as they read it. Imagine that modern English did not use white space to delimit words in texts. When we search for the word them in any corpus, we would fi nd both them and the mainland, which is not what we want. It is to avoid such meaningless corpus retrieval that segmentation is undertaken. As words are the basis of most corpus searching and retrieval tasks, such meaningless retrieval is a real problem in Chinese corpus linguistics. It is for this reason that in Chinese corpus linguistics, any string of characters in a corpus text must fi rst be converted into legitimate words, typically prior to any further linguistic analysis being undertaken (see Feng 2001 ; Xia et al. 2000 ) because “in computational terms, no serious Chinese language processing can be done without segmentation” (Huang et al. 1997: 47). 48 4 Corpora and Corpus Tools in Use

The segmentation tool we used to process the LCMC corpus is the Chinese Lexical Analysis System developed by the Institute of Computing Technology, Chinese Academy of Sciences. The core of the system lexicon incorporates a frequency dictionary of 80,000 words with part-of-speech information. The system is based on a multilayer hidden Markov model and integrates modules for word segmentation, part-of-speech tagging and unknown word recognition (see Zhang et al. 2002 ). The rough segmentation module of the system is based on the shortest paths method (Zhang and Liu 2002 ). The model, based on the two shortest paths, achieves a precision rate of 97.58 %, with a recall rate as high as 99.94 % (Zhang and Liu 2002 ). In addition, the average number of segmentation candidates is reduced by 64 times compared with the full segmentation method. The unknown word recognition module of the system is based on role tagging. The module applies the Viterbi algorithm to determine the sequence of roles (e.g. internal constituents and context) with the greatest probability in a sentence, on the basis of which template matching is carried out. The integrated ICTCLAS system is reported to achieve a precision rate of 97.16 % for tagging, with a recall rate of over 90 % for unknown words and 98 % for Chinese person names (Zhang and Liu 2002 ). However, the POS system is in part under-specifi ed, especially in the crucial area of aspect marking. For example, the system does not differentiate between the preposition zai ൘and the aspect marker zai ൘. Furthermore, as the system was trained using news texts, its performance on some text types (e.g. martial arts fi ction) is poor. For example, although dao 䚃is used much more frequently as a verb meaning “say” in martial arts fi ction than in other text types, it was tagged by the system as a classifi er or noun (i.e. “road”). As such, we decided to undertake post-editing of the processed corpus to classify all of the instances of the four aspect markers (le Ҷ, guo 䗷, zhe ⵰ and zai ൘). In addition, except for the three categories of news texts and the reports/offi cial documents, on which the system performs exceptionally well, all of the processed texts were hand checked and corrected. The post-editing improved the annotation precision to over 98 %. As a fi nal step, the post-edited corpus fi les were converted into XML format.

4.3 The Zhejiang University Corpus of Translational Chinese

This section will introduce the Zhejiang University Corpus of Translational Chinese (ZCTC ), the corpus which is built as a comparable counterpart of translated Chinese texts to the LCMC corpus. We will have a review on the technical details of the corpus design, sampling, encoding and markup, segmentation and annotation of ZCTC brieﬂ y. 4.3 The Zhejiang University Corpus of Translational Chinese 49

4.3.1 Corpus Design

The ZJU Corpus of Translational Chinese was created with the explicit aim of studying the features of translational Chinese in relation to non-translated native Chinese. It has modelled the Lancaster Corpus of Mandarin Chinese (LCMC) (see above). Both LCMC and ZCTC corpora have sampled fi ve hundred 2,000-word text chunks from 15 written text categories published in China, with each corpus amounting to one million words. Since the LCMC corpus was designed as a Chinese match for the FLOB corpus of British English (Hundt et al. 1998 ) and the Frown Corpus of American English (Hundt et al. 1999 ), with the specifi c aim of comparing and contrasting English and Chinese, the number of text samples and their proportions given in Table 4.4 are exactly the same as in FLOB and Frown . The ZCTC has taken the same genres and respective proportions of LCMC. As previously mentioned, the LCMC corpus has also followed the sampling period of FLOB / Frown by sampling written Mandarin Chinese within 3 years around 1991. While it was relatively easy to fi nd texts of native Chinese published in this sampling period, it would be much more diffi cult to get access to translated Chinese texts of some genres—especially in electronic format—published within this time frame. This pragmatic consideration of data collection forced us to modify the LCMC model slightly by extending the sampling period by a decade, i.e. to 2001, when we built the ZCTC corpus. This extension was particularly useful because the popularisation of the Internet and online publication in the 1990s made it possible and easier to access a large amount of digitalised texts. While English is the source language of the vast majority (99 %) of the text samples included in the ZCTC corpus, we have also included a small number of

Table 4.4 A comparison of LCMC and ZCTC corpora Genre LCMC tokens Proportion (%) ZCTC tokens Proportion (%) A 89,367 8.73 88,196 8.67 B 54,595 5.33 54,171 5.32 C 34,518 3.37 34,100 3.35 D 35,365 3.46 35,139 3.45 E 77,641 7.59 76,681 7.54 F 89,967 8.79 89,675 8.81 G 156,564 15.30 155,601 15.29 H 61,140 5.97 60,352 5.93 J 163,006 15.93 164,602 16.18 K 60,357 5.90 60,540 5.95 L 49,434 4.83 48,924 4.81 M 12,539 1.23 12,267 1.21 N 60,398 5.90 59,042 5.80 P 59,851 5.85 59,033 5.80 R 18,645 1.82 19,072 1.87 Total 1,023,387 100.00 1,017,395 100.00 50 4 Corpora and Corpus Tools in Use texts translated from other languages (e.g. Japanese, French, Spanish and Romanian) to mirror the reality of the world of translations in China. As Chinese is written as running strings of characters without white spaces delimiting words, it is only possible to know the number of word tokens in a text when the text has been tokenised. As such, the text chunks were collected at the initial stage by using our best estimate of the ratio (1:1.67) between the number of characters and the number of words based on our previous experience (McEnery et al. 2003 ). Only textual data was included, with graphs and tables in the original texts replaced by placeholders. A text chunk included in the corpus can be a sample from a large text (e.g. an article and book chapter) or an assembly of several small texts (e.g. for the press categories and humour). When parts of large texts were selected, an attempt was made to achieve a balance between initial, medial and ending samples. When the texts were tokenised, a computer program was used to cut large texts to approximately 2,000 tokens while keeping the fi nal sentence complete. As a result, while some text samples may be slightly longer than others, they are typically around 2,000 words. Table 4.4 compares the actual numbers of word tokens in different genres as well as their corresponding proportions in the ZCTC and LCMC corpora. As can be seen, the two corpora are roughly comparable in terms of both overall size and proportions for different genres. One difference from LCMC (version 1.0) is that the 500 sample texts in ZCTC are separately stored as XML fi les in the corpus which enables further statistical analysis. It is noteworthy of pointing out that the numbers of tokens in LCMC and its text genres given in Table 4.4 are based on the second version of LCMC which has been reorganised and annotated to cater for the need of this research project. These numbers are different from the fi rst version published by the European Language Resource Association (ELRA) and Oxford Text Archive (OTA). This will be discussed in detail later.

4.3.2 Encoding and Markup

The ZCTC corpus is marked up in Extensible Markup Language (XML) which is in compliance with the Corpus Encoding Standards (CES; see Ide and Priest-Dorman 2000 ). Each of the 500 data fi les has two parts: a corpus header and a body. As shown in Fig. 4.3 , the cesHeader gives general information about the corpus (publi- cationStmt) as well as specifi c attributes of the text sample (fi leDesc). Details in the publicationStmt element include the name of the corpus in English and Chinese, authors, distributor, availability, publication date and history. The fi leDesc element shows the original title(s) of the text(s) from which the sample was taken, individu- als responsible for sampling and corpus processing, the project that created the corpus fi le, date of creation, language usage, writing system, character encoding and mode of channel. The body part of the corpus fi le contains the textual data, which is marked up for structural organisation such as paragraphs (p) and sentences (s). Sentences are con- secutively numbered for easy reference. Part-of-speech annotation is also given in XML, with the POS attribute of the w element indicating its part-of-speech category. 4.3 The Zhejiang University Corpus of Translational Chinese 51

Fig. 4.3 The header ﬁ le of ZCTC

Figure 4.4 has shown a sample of an annotated paragraph in ZCTC chosen from genre A (press reportage). The XML markup of the ZCTC corpus is perfectly well formed and has been validated using Altova XMLSpy 2008, a comprehensive editing tool for XML documents. The XML elements of the corpus are defi ned in the accompanying Document Type Defi nition. The ZCTC corpus is encoded in Unicode, applying the Unicode Transformation Format 8-Bit (UTF-8), which is a lossless encoding for Chinese while keeping the XML fi les at a minimum size. The combination of Unicode and XML is a general trend and standard confi guration in corpus development, especially when corpora involve languages other than English (cf. Xiao et al. 2004 ).

4.3.3 Segmentation and POS Annotation

The ZCTC corpus is annotated using ICTCLAS2008, the latest release of the Chinese Lexical Analysis System developed by the Institute of Computing Technology, the Chinese Academy of Sciences. This annotation tool, which relies on a large lexicon and the hierarchical hidden Markov model (HMM), integrates word tokenisation, named entity identiﬁ cation, unknown word recognition as well as part-of-speech (POS) tagging. The ICTCLAS part-of-speech tagset distinguishes between 22 level 1 part-of-speech categories (see Table 4.5 ), which expand into over 80 level 2 and 3 categories for word tokens in addition to more than a dozen categories for symbols and punctuations. The ICTCLAS2008 tagger has been reported to achieve a precision rate of 98.54 % for word tokenisation. Latest open 52 4 Corpora and Corpus Tools in Use

Fig. 4.4 A sample of an annotated paragraph in ZCTC tests have also yielded encouraging results, with a precision rate of 98.13 % for tokenisation and 94.63 % for part-of-speech tagging.

4.3.4 The Upgraded Version of LCMC

As mentioned above, the ﬁ rst version of native Chinese corpus, LCMC (version 1.0), used for this research was originally part-of-speech annotated with the early version of ICTCLAS (version 1.0), whereas the translational corpus of ZCTC is processed with latest release of the same tool ICTCLAS2008 which was designed and upgraded in terms of the annotating accuracy. Due to the differences between the old and new versions of ICTCLAS (e.g. different tagsets used), we reprocessed LCMC with the new ICTCLAS tool. While in LCMC (1.0) all the texts of the same genre were put together, in the second version we decide to separate the texts and 4.4 Parallel Corpora Used in This Research 53

Table 4.5 Level 1 part-of- Level 1 POS category Defi nition speech categories a Adjective b Non-predicate noun modifi er c Conjunction d Adverb e Interjection f Space word h Prefi x k Suffi x m Numeral and quantifi er n Noun o Onomatopoeia p Preposition q Classifi er r Pronoun s Place word t Time word u Auxiliary v Verb w Symbol and punctuation x Non-word character string y Particle z Descriptive adjective store them as independent XML fi les. This is to ensure that the upgraded LCMC and ZCTC are completely matchable to each other in order to analyse the translated texts in relation to their native counterparts respectively.

4.4 Parallel Corpora Used in This Research

Apart from the monolingual native and translational corpora ( FLOB , LCMC 2.0 and ZCTC) introduced in the previous sections, we have also used two English- Chinese parallel corpora with the main aim of looking into the degree of source language interference in translational language. The two parallel corpora used in this research are Babel and GCEPC , which are both aligned at the sentence level.

4.4.1 The Babel English-Chinese Parallel Corpus

The Babel English-Chinese Parallel Corpus , built by the Department of Linguistics and English Language of Lancaster University, covers mixed genres, consisting of 327 English articles and their translations in Mandarin Chinese. Of these, 115 texts 54 4 Corpora and Corpus Tools in Use

(121,393 English words, 135,493 Chinese words) were collected from the World of English (an English journal published in China) between October 2000 and February 2001, while the remaining 212 texts (132,140 English words, 151,969 Chinese words) were collected from the Time magazine from September 2000 to January 2001. The corpus contains a total of 253,633 English words in the source texts and 287,462 Chinese tokens in the translations. Paragraphs and sentences in the SL and TL subcorpora are aligned and, respectively, POS annotated with CLAWS (an English POS annotation tool) and ICTCLAS 1.0 (see Xiao 2005).

4.4.2 The General Chinese-English Parallel Corpus

The General Chinese-English Parallel Corpus ( GCEPC ), which was created by Beijing Foreign Studies University, is the largest existing parallel corpus of English and Chinese. This is a Chinese-English bidirectional parallel corpus containing about 20 million English words and Chinese characters. It has four subcorpora, namely, Chinese-to-English literature, Chinese-to-English nonliterature, English- to-Chinese literature and English-to-Chinese nonliterature (Wang 2004 ; Wang and Qin 2010 ). As we are interested in how Chinese translations are affected by English source texts, only the two English-to-Chinese subcorpora will be used, amounting to 12 million words/characters, 60 % of which are for English-Chinese literature and 40 % for English-Chinese nonliterature (cf. Wang 2004 : 40).

4.5 Corpus Analytical and Statistical Tools

A number of corpus analytical and statistical tools have been used in our research, including English or Chinese monolingual concordancers, English-Chinese parallel concordancers and others. The following sections will give a brief introduction to these tools to help the novice readers to use and analyse the corpora which are available for downloading with the book (see Appendix 3 ).

4.5.1 Xaira

Xaira stands for the full name XML Aware Indexing and Retrieval Architecture, which was once a version of SARA (SGML-Aware Retrieval Application), the text searching software originally developed at Oxford University Computing Services for use with the British National Corpus (BNC). Different from SARA, Xaira is no longer bound with the use of BNC but was entirely rewritten as a general-purpose XML search engine, which will operate on any corpus of well-formed XML documents. 4.5 Corpus Analytical and Statistical Tools 55

As a unique corpus searching and analysing tool, Xaira has some advantages over some other publicly available text searching tools. First of all, Xaira is a truly XML- aware software, capable of not only searching for the annotated texts but also searching for the XML markups themselves. This feature is particularly useful when searching for and analysing annotated markups. Second, Xaira surpasses other search engines in that it can display complete sentences in the KWIC (keyword in context) search, while most other existing concordancers only reveal a certain number of words to the left and right of the keyword. Another advantage of Xaira to our research is that it has full Unicode support. This means you can use it to search and display text in any language (certainly necessary for dealing with Chinese characters), provided you have a suitable Unicode font installed on your system. Furthermore, Xaira is an open-source software freely distributed to the public. The current version of Xaira is release 1.26, and all versions are available to download from the open-source freeware website SourceForge (http://xaira.sourceforge.net/ ). One of the disadvantages of Xaira for the use of research is that it is relatively complex to index the corpus before searching. The three monolingual corpora used in this research ( FLOB , LCMC and ZCTC) are all marked up in XML format, which makes it possible to use Xaira to compare the three corpora. In the following part, we will try to clarify some of the indistinct issues in its index tutorial. Xaira has three components: an index toolkit, a server and a Client program. The indexer tool is used to index a corpus marked up in XML and to create a corpus server which the user can access through the Client program, either locally or remotely, over a network. A handy indexing wizard is included (File – Index wizard in the main menu) that can facilitate the indexing task. Figure 4.5 shows the menus of the indexer toolkit. The most important menu is Tools, which has three parts. The process of indexing a corpus follows the steps in the order given, though not all steps are required, depending on the level of detail of the corpus markup. Part A sets up a parameter fi le for the server and makes a corpus header. Parameter fi l e opens the parameters dialogue box which allows the user to defi ne the components of a corpus, for example, the corpus root directory, the text and index folders, the corpus header and the bibliography. Xcorpus fi le creates a fi le that provides a description of the corpus to the Client and server and can be opened in the Client or simply by double clicking it. The File list dialogue box allows the user to decide which fi les to include in the corpus. Parse all is an optional step that parses all corpus fi les selected to see if there are any XML errors. All errors are logged in the indexer window, which must be corrected before the corpus can be indexed. Make header makes a copy of the corpus header while parsing and validating the corpus. Like any XML-processing software, Xaira is not particularly tolerant of errors, so all XML fi les must be well formed. Refresh (selected or all fi elds) is only required when one or more fi elds in Part B have been edited manually so as to refresh the edited fi eld(s) in the corpus header. Touch is an optional step which forcibly acti- vates the Save button in the toolbar. Preprocess is an optional but useful step which helps the user to add simple XML markup to plain text or to migrate SGML into XML. If a corpus has an XML element encoding bibliographic information for individual texts, Make bibliography can be used to build a bibliography. 56 4 Corpora and Corpus Tools in Use

Fig. 4.5 The index toolkit of Xaira

Part B defi nes various parameters. The Tag usage dialogue box allows the user to add descriptive glosses to the attribute names, to build a code book (i.e. a collection of attribute values) for particular attributes (e.g. part-of-speech), to set indexing policies which affect how Xaira indexes mark up and to designate a particular attribute as a global or as a local attribute. The Languages dialogue allows the user to add/remove language support for different languages. The Special tags command designates tags for language detection, word tokenisation and taxonomy. In the Additional key dialogue box, the user can select the additional information (e.g. part-of-speech and lemma) to be stored with each indexed word if a corpus is annotated at the token level. An additional key searches for words based on some attribute value in the XML markup rather than on the word form per se. Taxonomy starts a dialogue which allows the user to classify texts using different taxonomies. The References dialogue specifi es how a concordance is referenced, i.e. its location in the corpus (e.g. sentence or line number). The Bibliographic dialogue allows the user to enter bibliographic information in the corpus header (but it is not working in this release). The Code ranges settings specify which characters appear on the soft Unicode keyboard which allows the user to enter characters that are not on the regu- lar keyboard. Part C starts the indexer tool to build an index for the corpus. It also gives the user an option to create a text fi le showing the frequency of each headword for each lemmatisation scheme when the corpus is indexed, as well as some command line 4.5 Corpus Analytical and Statistical Tools 57 options passed to the indexer tool. When the parameters in Parts A and B are set, the user can select Index – Run to start the indexing process. If no errors are found in the corpus fi les, the indexer tool exits with code 0; otherwise, it exits with an error code, with more extensive error information recorded in the corpus log. The corpus server set up using the procedure described above is ready for local access through the Client, but the user can also set up a server for remote access by selecting File – New remote corpus . The Client provides an interface between the user and a local or remote corpus server. Figure 4.6 shows a part of the Client toolbar. Xaira allows a range of query types: Quick query (1), Word query (3), Phrase query (4), Addkey query (5), Pattern query (6), XML query (7) and CQL query using the XML-based Corpus Query Language (9). Quick query is the same as Phrase query. A search pattern can be entered using the keyboard or the soft Unicode keyboard (2). These query types can be combined using Query builder (8), which is suffi ciently powerful to build complex queries that can meet most corpus exploration needs. The user can also access these query types by selecting File – New query in the main menu. Addkey query is only available if a corpus is annotated at the token level (e.g. part-of-speech tagging and lemmatisation) and the Additional key is defi ned when the corpus is indexed. The Edit button (10) is a handy tool that allows the user to edit a query instead of entering a similar query from scratch. Figure 4.7 shows the other part of the Client toolbar of Xaira. If there are less than 100 matches from a query, the dialogue box will show all the matches. If more than 100 matches are found when a query is made, a dialogue box appears that indicates the total number of hits and prompts the user to download the required number (or all of them); otherwise the hits are displayed directly. The results can be displayed in the Page mode (i.e. one hit per page, using Page Up/Page Down on the keyboard to turn pages) or the Line (i.e. KWIC) mode. The user can switch between the two modes by clicking button (6) in Fig. 4.3 . Button (7) is used to display the result in the plain text or XML format. The scroll box (8) specifi es the amount of context to be displayed. The align dropdown (9) controls the direction of the text fl ow in the result. If a bibliography is built when a corpus is indexed, clicking on button (1) shows the bibliographic information of a selected concordance. The user can use the Sort button (2) to sort concordances displayed in the KWIC mode. Right clicking a concordance allows the user to copy and select the line or to expand its context, among other things. The Thin command (3) can be used to keep or discard

Fig. 4.6 The Client toolbar of Xaira (1)

Fig. 4.7 The Client toolbar of Xaira (2) 58 4 Corpora and Corpus Tools in Use the selected concordances, thus allowing the user to edit the result. A query can be saved by selecting File – Save (as), or exported in the XML format for use in other text editors or word processors by selecting Query – Listing in the menu. In addition to providing a user interface, the Client program can also be used to analyse language use, for example, by extracting collocations/colligations and examining distribution patterns if partitions (i.e. subcorpora) have been defi ned. The Collocation button (4) opens the collocation dialogue box, which allows the user to defi ne the window span and to select the statistical measure (MI or z score), the minimum score/frequency, etc., for extracting collocations/colligations. The Analysis button (5) shows the distribution of a query across subcorpora, which can be saved as a list. A graphic presentation (as a pie or bar chart) of the distribution is also available. Subcorpora are defi ned by selecting Text – Defi ne partition in the menu. A defi ned partition can be opened by selecting Text – Open partition or registered with the corpus index permanently. Xaira has many other useful features not covered in this section, which include user-defi ned style sheets, colour books, annotation, etc.

4.5.2 WordSmith Tools

The other monolingual corpus searching and analysing tool we used for this research is WordSmith Tools (version 5.0), a classic package of corpus-analysing programs developed by the British linguist Mike Scott and published by Oxford University Press since 1996. It is a Unicode-compliant multifunctional suite of programs including Concord, Wordlist, Keyword and many additional features (e.g. corpus building based on webpages, text transfer, etc.). WordSmith Tools is used in our research mostly for the purpose of retrieving word clusters and keyword analysis . As WordSmith Tools is a commercial software package, which has very detailed online and built-in manuals guiding the user to make use of the software, we will not give any verbose introductions to the basic functions of WordSmith Tools, but rather will say a few words about the less discussed features of word cluster and keyword analysis . The term “word cluster”, also known as “lexical bundle”, “n-gram”, “multiword unit” or “prefab”, refers to the sequence of words which are found repeatedly together in a corpus or a text and which represent the particular pattern repeatedly occurring in the corpus or text. In WordSmith Tools, word clusters can be identiﬁ ed through Concord or Wordlist in different ways: Concord only processes concordance lines, while Wordlist processes whole texts. Let us ﬁ rst examine how to get word clusters for a certain node word (the word “why” is chosen for illustration purpose) in the FLOB corpus via the Concord feature of WordSmith Tools. As shown in Fig. 4.8 , the current tab window is the “concordance”, from the left bottom of which we can see there are 504 matches for the node word “why”. After clicking on the tab “cluster”, the new window (Fig. 4.9 ) will show 20 three-word (default) clusters. Certainly, as shown in Fig. 4.10 , the 4.5 Corpus Analytical and Statistical Tools 59

Fig. 4.8 The Concord window of WordSmith

default 3-word clustering algorithm can be redeﬁ ned by selecting from the menu Compute – Clusters in order to retrieve other types of n-grams, for example, 2-word or 4-word clusters, or to set up a range of the number of words in clusters (e.g. 2- to 6-word clusters). The second method of retrieving word clusters from a corpus is through the Wordlist function, for which an index has to be set up for the whole corpus in question. Figure 4.11 demonstrates the settings of index, in which the default minimal frequency is 5 for 3- word clusters , and the calculation stops at sentence break. The Relationship key at the bottom can be used to further conﬁ gure the index (see Fig. 4.12 ), such as calculating parameters, thresholds and inclusion or exclusion of certain words (through Word to process and Omissions ) In WordSmith, indexing is realised through the same Wordlist feature as making the wordlist of a corpus, but a different tab is involved: the Make/Add to index tab (see Fig. 4.13 ). The result of indexing is shown in Fig. 4.14 , which looks similar to a common wordlist but contains more information about the location of each word in the corpus other than the frequency of the word. If we select from the menu Compute – Clusters , a new menu of cluster choices will be open (Fig. 4.15 ). Suppose 60 4 Corpora and Corpus Tools in Use

Fig. 4.9 The cluster window of WordSmith Tools

Fig. 4.10 Cluster settings in WordSmith Tools 4.5 Corpus Analytical and Statistical Tools 61

Fig. 4.11 General settings in WordSmith Tools

Fig. 4.12 The relationship choices for index settings

Fig. 4.13 Wordlist of WordSmith Tools

Fig. 4.14 The result of an index 4.5 Corpus Analytical and Statistical Tools 63

Fig. 4.15 Cluster choices in WordSmith

it is the 3- word clusters that we want, we can just maintain the default value. The result for 3-word clusters in FLOB is shown in Fig. 4.16 . As mentioned above, we also used WordSmith Tools for keyword and key word- class analysis. Keywords are words which occur unusually frequently or infre- quently in comparison with some kind of reference corpus. The “unusually frequent” words are also called (positive) keywords, while the “unusually infrequent” ones are called negative keywords. The “keyness” of keywords is often measured by the chi- square or log-likelihood test ( LL ). Figure 4.17 shows the settings for keywords in WordSmith. The user can select from the calculating formulae available and deﬁ ne the thresholds for the keywords to be extracted, with the default formula being the LL test, the maximal p value of 0.000001 and the minimal frequency of 3. All values are adjustable according to the corpus size and the need of research. If one wants to take into account the negative keywords, the maximal number wanted should be set as large as possible, since negative keywords show up at the end of the keyword list (marked in red colour). The reference corpus used for keyword analysis is essential to the value of the keyword list, because different reference corpora will produce different keywords. Let us take an example from a corpus of Jane Austin’s novels. When calculating the keywords of the corpus in question, if the reference corpus is the novels written by Austin’s contemporaries, the keywords we get are most possibly related to the subjects and style of Austin’s works; but if we use the British National Corpus (BNC) as the reference corpus, the keywords may probably reﬂ ect the linguistic features of 64 4 Corpora and Corpus Tools in Use

Fig. 4.16 3-word clusters in FLOB corpus

Fig. 4.17 The settings for Keywords 4.5 Corpus Analytical and Statistical Tools 65 nineteenth-century English. As such, the appropriateness of a reference corpus relies on the purpose and the questions of the research. In general, a reference corpus should be bigger than the corpus in question, but it does not mean a corpus of similar size cannot be used as reference corpus. As Tribble (1999 : 171) noted, the size of reference corpus does not matter very much. An experiment given by Scott and Tribble ( 2006 ) elaborates this point: different reference corpora were used for keyword analysis of Shakespeare’s Romeo and Juliet . They found whichever reference corpus (whether it is Shakespeare’s all works, Shakespeare’s all drama, Shakespeare’s all tragedies and BNC), the core part of the keyword list of Romeo and Juliet remains the same, including proper names such as “Benvolio, Romeo, Juliet and Mantua” and even common nouns like “banished, county, love and night”. The speciﬁ c procedures of keyword analysis in WordSmith begin with building wordlists for both the corpus in question and the reference corpus and then applying the Keyword feature to retrieve keywords. The software package itself has detailed online and built-in manuals as for the Keyword function. In addition, there are also some corpus linguistics textbooks spending some chapters on those procedures. It would not be necessary for us to add more verbosity to the topic. Let us just end this section by pointing out that keyword analysis is a very useful analytical method in corpus linguistics . Its application is not limited by content analysis but stretches far to other research areas, for example, stylistic studies (Xiao and McEnery 2005b ). In its advanced form, the key word-class analysis for POS-annotated corpus is obviously very interesting.

4.5.3 ParaConc

ParaConc is a bilingual or multilingual concordancer developed by American linguist Michael Barlow (Barlow 2009). The concordancer is featured for its capability of concordancing up to four different languages or showing search results for one source text and up to three different translations. It is also ﬂ exible in deﬁ ning languages, size of the concordance lines, showing or hiding the markups and using wildcards in searching. The program was initially created for the purpose of concordancing aligned parallel (source text and translation) corpus, which is indeed very useful for the researcher to probe into the hidden features, patterns and structures in the translated text in contrast to the source text, but it has been widely used in contrastive analyses, language learning, translation studies and translation training. We will use ParaConc to process the two parallel corpora introduced previously, the Babel and GCECPC corpus. Figure 4.18 shows a sample concordance for the word “news” in the Babel corpus by using ParaConc. 66 4 Corpora and Corpus Tools in Use

Fig. 4.18 A sample parallel concordance in ParaConc

In addition to the functions of searching, indexing, making wordlist and creating keyword list, the three corpus concordancers as introduced in this section also have statistical functions, which are used by us in presenting simple and basic statistical data. Whenever it is necessary for complex statistical analysis, we will turn to professional statistical analysis software, for instance, SPSS (Statistical Product and Service Solutions). We have also written some customised computer programs in PERL (Practical Extraction and Retrieval Language) to cater for our speciﬁ c research needs. With all these facilities, we will then go on to explore the distinct linguistic features of translational Chinese in the following chapters. We will start with the macro-level features in Chap. 5 . Chapter 5 The Macro-Statistic Features of Translational Chinese

In this chapter, we will investigate the macro-statistic features of translational Chinese based on contrastive analyses of the corpus of translational Chinese (ZCTC) and the corpus of non-translational or native Chinese (LCMC). These macro- statistic linguistic features involve a wide range of corpus linguistic parameters, such as lexical density, textual information load, high-frequency word s, low- frequency word s, average word length, average sentence length, average sentence segment length, average paragraph length and word clusters, which will be analysed and discussed in detail in the following sections.

5.1 Lexical Density and Textual Information Load

As reviewed in Chap. 2, Laviosa (1998b ) studies the distinctive features of translational English in relation to native English (as represented by the British National Corpus ), fi nding that translational language has four core patterns of lexical use: (1) a relatively lower proportion of lexical words over function words, (2) a relatively higher proportion of high-frequency words over low-frequency word s, (3) a relatively greater repetition of the most frequent words and (4) a smaller vocabulary frequently used. These patterns of lexical use in translational English are supported by Hu’s ( 2006 ) similar fi ndings in translated Chinese fi ction. This section discusses the parameters used in Laviosa ( 1998b ) in an attempt to fi nd out whether the core patterns of lexical use that Laviosa observes in translational English also apply in translational Chinese in general. We will fi rst compare lexical density and textual information load in non-translated and translated Chinese and then examine the frequency profi les of the two corpora in the following sections. There are two common measures of lexical density. Stubbs (1986 : 33, 1996 : 172) defi nes lexical density as the ratio between the number of lexical words (i.e. content

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 67 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_5 68 5 The Macro-Statistic Features of Translational Chinese word s) and the total number of words. This approach is taken in Laviosa ( 1998b ). As our corpora are part-of-speech tagged, frequencies of different POS categories are readily available. The other approach commonly used in corpus linguistics is the type token ratio (TTR), i.e. the ratio between the number of types (i.e. unique words) and the number of tokens (i.e. running words). However, since the TTR is seriously affected by text length, it is reliable only when texts of equal or similar length are compared. To remedy this issue, Scott (2004 ) proposes a different strategy, namely, using a standardised type token ratio (STTR ), which is computed every n (the default setting is 1,000 in the WordSmith Tools) word as the Wordlist application of WordSmith goes through each text fi le in a corpus. The STTR is the average type token ratio based on consecutive 1,000-word chunks of text (Scott 2004 : 130). It appears that lexical density defi ned by Stubbs ( 1986 , 1996 ) measures textual informational load whereas the STTR is a measure of lexical variability, as refl ected by the different ways they are computed. Let us fi rst examine the Stubbs-style lexical density in native and translational Chinese. Xiao and Yue ( 2009 ) fi nd that the lexical density in translated Chinese fi c- tion (58.69 %) is signifi cantly lower than that in native Chinese fi ction (63.19 %). Does this result also hold for other genres or for Mandarin Chinese in general as represented in the two balanced corpora in the present study? Figure 5.1 and Table 5.1 show the scores of lexical density in the fi fteen genres covered in the ZCTC and LCMC corpora as well as their mean scores. As can be seen in Table 5.1 , the mean lexical density in LCMC (66.93 %) is considerably higher than that in ZCTC (61.59 %). This mean difference −5.34 is statistically signifi cant ( t = −4.94 for 28 d.f., p < 0.001). It is also clear from the fi gure that all of the 15 genres have a higher lexical density in native than translated Chinese, which is statistically signifi cant for nearly all genres (barring M, i.e. science fi ction), as indicated by the statistic tests in Table 5.1 . These fi ndings are in line with Laviosa’s ( 1998b ) observations of lexical density in translational English.

Fig. 5.1 Lexical density 80.00 in ZCTC and LCMC 70.00 60.00 50.00 LCMC 40.00 ZCTC 30.00 Lexical density 20.00 10.00 0.00 ABCDEFGHJKLMNPR Genre 5.1 Lexical Density and Textual Information Load 69

Table 5.1 Mean differences in lexical density across genres Genre t score Degree of freedom Signiﬁ cance level Mean difference A −2.43 86 0.017 −1.446 B −3.35 52 0.002 −2.180 C −6.96 32 <0.001 −5.144 D −8.07 32 <0.001 −7.307 E −4.93 74 <0.001 −4.703 F −9.79 86 <0.001 −7.934 G −4.05 152 <0.001 −2.184 H −9.61 58 <0.001 −12.21 J −9.13 158 <0.001 −4.777 K −5.64 56 <0.001 −5.193 L −6.28 46 <0.001 −5.984 M −0.44 10 0.667 −1.056 N −13.66 56 <0.001 −9.122 P −2.29 56 0.026 −1.987 R −8.85 16 <0.001 −9.215 Mean −4.94 28 <0.001 −5.342

ZCTC 30 LCMC STTR

0 J P F E L B R C N A D H K G M Mean Genre

Fig. 5.2 Standardised TTR in ZCTC and LCMC

However, if lexical density is measured by the STTR , then the LCMC corpus as a whole has a slightly higher STTR than ZCTC (46.58 vs. 45.73), but the mean difference (−0.847) is not statistically signiﬁ cant (t = −0.573 for 28 d.f., p = 0.571). This result is further conﬁ rmed by a closer look at individual genres as dispalyed in Fig. 5.2. As can be seen, the differences for most genres are marginal. While some 70 5 The Macro-Statistic Features of Translational Chinese

Fig. 5.3 Standardised TTR 49.00 in ZCTC and LCMC 48.00 47.00 46.00 45.00 STTR 44.00 43.00 42.00 A-J K-R LCMC 46.00 47.91 ZCTC 44.51 47.22

2.5

1.5 ZCTC 1 LCMC

Lexical-function word ratio 0.5

0 J F P E L B C R D H A G K N M Mean Genre

Fig. 5.4 Lexical-to-function word ratios in ZCTC and LCMC genres display a higher STTR in native Chinese, there are also genres with a higher STTR in translated Chinese. If we combine the genres A–J into a general nonfi ction or non-literature category and the remaining genres K–T into a fi ction or literature category, we can also only fi nd marginal differences between the two categories (see Fig. 5.3 ). This fi nding extends Xiao and Yue’s ( 2009 ) observation of translated Chinese fi ction to Mandarin Chinese in general. In terms of lexical versus function words, a signifi cantly higher ratio of lexical over function words is found in native Chinese than in translated Chinese (2.08 vs. 1.64, t = −4.88 for 28 d.f., p < 0.001, mean difference = −0.441). As can be seen in Fig. 5.4 , 5.2 Analyses of Word Frequencies and Mean Word Length 71

Table 5.2 Mean differences in lexical-function word ratio across genres Genre t score Degree of freedom Signiﬁ cance level Mean difference A −2.60 86 0.011 −0.132 B −3.34 52 0.002 −0.228 C −6.84 32 <0.001 −0.497 D −7.94 32 <0.001 −0.588 E −5.05 74 <0.001 −0.438 F −9.10 86 <0.001 −0.590 G −3.98 152 <0.001 −0.167 H −9.88 58 <0.001 −1.125 J −8.96 158 <0.001 −0.435 K −5.42 56 <0.001 −0.364 L −6.23 46 <0.001 −0.431 M −0.59 10 0.571 −0.097 N −13.01 56 <0.001 −0.730 P −2.34 56 0.023 −0.139 R −8.46 16 <0.001 −0.664 Mean −4.88 28 <0.001 −8.441

which gives the lexical-to-function word ratios in ZCTC and LCMC , all genres have a higher ratio in native Chinese than in translated Chinese, and the mean differences for all genres other than science fi ction (M) are statistically signifi cant (see Table 5.2 ), especially in reports and offi cial documents (H), adventure fi ction (N) and humour (R). This result, on the one hand, is in line with Xiao and Yue’s (2009 ) observation of translated Chinese fi ction and further confi rms Laviosa’s (1998b : 8) initial hypothesis that translational language has a relatively lower proportion of lexical words over function words; on the other hand, if we measure lexical density in terms of textual information load, i.e. the proportion of lexical words in the corpora, we can fi nd translated Chinese has lower lexical density than non-translated Chinese, but if we measure lexical density in terms of lexical variability or standardised type token ratio, there is no statistically signifi cant difference between translated and native Chinese.

5.2 Analyses of Word Frequencies and Mean Word Length

Laviosa ( 1998b ) deﬁ nes “list head” or “high-frequency words ” as every item which individually accounts for at least 0.10 % of the total occurrences of words in a corpus. In Laviosa’s study, 108 items were high-frequency words, most of which were function words. In our study, we also deﬁ ne high-frequency words as those with a minimum proportion of 0.10 %. But the numbers of items included can vary depending on the corpus being examined. In the mean time, we will also look into the 72 5 The Macro-Statistic Features of Translational Chinese

Table 5.3 Frequency proﬁ les Type LCMC ZCTC of ZCTC and LCMC Number of items 108 114 Cumulative proportion % 35.70 % 40.47 % Repetition rate of high-frequency words 2,870.37 3,154.37 Ratio of high-to-low-frequency words 0.5659 0.6988

500 450 400 350 300 250 200 150 100 Frequency analysis Frequency 50 0 >0.5% >0.1% >0.07% >0.05% >0.03% >0.02% >0.01% LCMC 14 95 52 96 282 401 474 ZCTC 15 94 60 96 266 390 391

Fig. 5.5 High-frequency words in LCMC and ZCTC low- frequency words which are defi ned as the words having occurrences of less than six in the corpora. In addition to high- and low-frequency words, mean word length is also discussed in this section. Table 5.3 shows the frequency profi les of the translated and native Chinese corpora. As can be seen, while the numbers of high-frequency words are very similar in the two corpora (114 and 108, respectively), high-frequency words account for a considerably greater proportion of tokens in the translational corpus (40.47 % in comparison to 35.70 % for the native corpus). The ratio between high- and low- frequency words is also greater in translated Chinese (0.6988) than in native Chinese (0.5659). Laviosa ( 1998b ) hypothesises on the basis of the results of lemmatisation that there is less variety in the words that are most frequently used. As Chinese is a non-infl ectional language, lemmatisation is irrelevant; and as noted earlier, the standardised type token ratio as a measure of lexical variability is very similar in translated and native Chinese. Nevertheless, it can be seen in Table 5.3 that high-frequency word s display a much higher repetition rate in translational than native Chinese (3,154.37 versus 2,870.37). Figure 5.5 illustrates the distribution of high-frequency words with a minimum percentage of 0.5, 0.1, 0.07, 0.05, 0.03, 0.02 and 0.01 % of the respective corpus. As can be seen from Fig. 5.1 , the numbers of ultra-high-frequency (greater than 0.05 %) words are very similar in LCMC and ZCTC as these words are the core vocabulary items, including most function words, which are commonly used in both native and 5.2 Analyses of Word Frequencies and Mean Word Length 73

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00% Freq.1 Freq.2 Freq.3 Freq.4 Freq.5 LCMC 43.22% 13.20% 7.92% 5.34% 3.82% ZCTC54.95% 14.49% 8.72% 5.72% 4.34%

Fig. 5.6 Low-frequency words in LCMC and ZCTC translational Chinese. We noted earlier that, in terms of word tokens, translational Chinese has a higher ratio between high- and low-frequency words, but as Fig. 5.5 shows, in terms of word types, native Chinese uses more high-frequency words, particularly sub-high-frequency words (i.e. between 0.05 and 0.01 %). This tendency is reversed in low-frequency words . Figure 5.6 shows the distribution of words with a frequency of 1–5 in the native and translational corpora, where the percentage refers to the proportion of the types of words with a particular frequency in total word types. It is clear from the fi gure that all of these low-frequency words display a high proportion in translational than native Chinese, and the lower the frequency is, the more marked the contrast is between the two corpora. The discussions above suggest that translational Chinese, while demonstrating a preference for ultra-high-frequency words, also makes less frequent use of sub- high- frequency words but instead makes more frequent use of low-frequency words . Hence, in terms of vocabulary use in general, translational Chinese does not necessarily demonstrate a simple tendency for simplifi cation . In addition, as illustrated in Fig. 5.7 , the mean word length in translational Chinese is marginally greater than in native Chinese (1.59 vs. 1.57, a statistically insignifi cant difference), which is true in both non-literary (1.63 vs. 1.61) and literary (1.47 vs. 1.42) texts, with an even more marked contrast between native and translational Chinese in literary texts possibly because literary genres contain more proper nouns such as personal names and place names, which are longer than similar words in native Chinese. Although the difference of mean word length between translated and native Chinese is not statistically signifi cant, translated Chinese demonstrates a larger standardised variance than native Chinese (0.72 vs. 0.68), showing that the difference among translated Chinese texts themselves is bigger than native texts. 74 5 The Macro-Statistic Features of Translational Chinese

1.65

1.6

1.55

1.5 LCMC 1.45 ZCTC

1.4

1.35

1.3 Non-literary Literary Mean

Fig. 5.7 Mean word length in LCMC and ZCTC

Table 5.4 Proportions of words of various lengths in LCMC and ZCTC Non-literary texts Literary texts Mean score Length LCMC ZCTC LCMC ZCTC LCMC ZCTC 1 syllable 46.76 46.06 62.45 58.61 50.68 49.14 2 syllables 47.65 48.04 34.02 37.5 44.25 45.45 3 syllables 3.60 3.90 2.54 2.73 3.34 3.62 4 syllables 1.59 1.43 0.91 1.05 1.42 1.34 5 syllables 0.31 0.37 0.06 0.09 0.25 0.30 6+ syllables 0.09 0.19 0.01 0.02 0.07 0.15

Table 5.4 shows the distribution of words of various lengths in LCMC and ZCTC across two broad categories, namely, literary and non-literary texts, and their mean scores. As the subcorpora are of different sizes, relative frequencies in the form of percentages will be compared. As can be seen from Table 5.4 , no matter whether native and translational corpora are taken as a whole, or the two broad text categories are considered separately, monosyllabic and quadrisyllabic words are generally more frequent in native Chinese. A keyword analysis suggests that monosyllabic words are more frequent in LCMC because native Chinese makes more frequent use of Chinese surnames, which are typically monosyllabic, as well as high-frequency dang ފ monosyllabic words such as ݳ yuan “Chinese currency unit” and “(Communist) Party”, though many monosyllabic function words are more frequently used in the translational corpus, e.g. the structural auxiliary Ⲵ de and personal pronouns ֐ ni “you”, ᡁ wo “I, me” and ྩ ta “she, her”, which are all negative keywords in LCMC in relation to ZCTC. Quadrisyllabic words are more frequently used in LCMC because non-literary texts in native Chinese tend to make signiﬁ cantly more frequent use of idioms, which are typically of the four-character 5.3 Mean Sentence and Paragraph Length 75 mould (see Sect. 6.6 for a detailed discussion of idioms). In contrast, disyllabic and trisyllabic words are more frequent in translated texts. The translational tendency for long words is particularly marked in words containing ﬁ ve or more syllables, though these words per se are infrequent in both native and translational Chinese.

5.3 Mean Sentence and Paragraph Length

As reviewed in Chap. 3 , mean sentence length has been used by some researchers in discussion of translation universals, particularly in discussion of the explicitation hypothesis of translational language; that is, it is hypothesised that translated texts usually demonstrate longer sentence length than native texts written in the same language. We will look into this hypothesis by measuring and comparing the mean sentence/paragraph length and the mean sentence segment length . Figure 5.8 shows the mean sentence length scores of various genres in native and translated Chinese. As can be seen, while native Chinese has a slightly greater mean sentence length, the mean difference between ZCTC and LCMC is not statistically signiﬁ cant. In both native and translated Chinese, genres such as humour (R) use relatively shorter sentences, whereas genres such as academic prose (J) use longer sentences; in some genres there is a sharp contrast between native and translated Chinese (e.g. science ﬁ ction M), whereas in other genres the differences are less marked (e.g. academic prose J and press reportage A). It appears, then, that mean sentence length is more sensitive to genre variation than being a reliable indicator of native versus translational language.

15 LCMC ZCTC 10 Mean sentence length 5

0 J F P E L C R B D A G H K N M Mean Genre

Fig. 5.8 Mean sentence length in ZCTC and LCMC 76 5 The Macro-Statistic Features of Translational Chinese

The above analysis takes full stops, question marks, exclamatory marks and semicolons as marks of sentential ending, as traditionally taken in corpus linguistics , though, as a matter of fact, there are very often several sentence segments which have relatively complete meanings in Chinese sentences separated by those sentential ending marks. This is because in modern written Chinese it is not uncom- mon to connect semantically complete sentence segments with commas. These sentence segments can be grammatically full clauses or phrasal structures, which are sometimes called “sentence segments” or “blocks”. Such a sentence consisting of a number of sentence segments or blocks is in fact a special sequence of sentential sections or blocks (Liu 1991 : 112). Chen ( 1994 ) fi nds that three quarters of sentences ending with a full stop and semicolon contain two or more complete sentence segments. As the following example shows, while the Chinese source text (1a) only contains one sentence, it actually expresses three relatively complete meanings, and as such, three sentences are used in the English translation (1b). (1a) ӪԜབྷཊ䙊䗷⭥ᖡ䇔䇶ਦ䭖␫, (2) ቔަ൘2001ᒤࠝ⵰ǉগ㱾㯿嗉Ǌѝި (䳵␵ᒭⲴьᯩ᜿䊑, ཪлॾӪц⭼ㅜаᓗྕᯟ঑ᴰ֣㖾ᵟᤷሬ྆ਾ, (3 ਴ൠ㴲ᤕ㘼㠣Ⲵ䚰㓖, ᴤᘛ䙏ሶԆ᧘ੁޘ⨳㡎ਠǄ (1b) Most people know Tim Yip through fi lms. (2) In particular, in 2001 he became the fi rst ever person from the Chinese world to win the US Academy Award for Best Art Direction, received for the elegant Oriental imagery he brought to Crouching Tiger, Hidden Dragon. (3) Since then, demand for his services has gone into hyperdrive, accelerating the spread of his fame and appeal worldwide. Hence, Wang and Qin (2010 : 169) argue that for languages that are characterised by parataxis such as Chinese (Liu 1991 ), sentence segment length is more meaningful than sentence length. In this book, the mean sentence segment length (MSSL ) is calculated by dividing the total number of words in the corpora or genres with the sum of sentence ending marks and commas. Table 5.5 gives the respective statistics of the two corpora, in which the “total words” means the number of “tokens used for wordlist” in WordSmith wordlist

Table 5.5 Mean sentence segment length in LCMC and ZCTC Genre Sentence Comma Sentence segments Total words MSSL LCMC News 7,629 12,926 20,555 146,881 7.15 General prose 18,614 31,854 50,468 347,731 6.89 Academic prose 6,330 10,211 16,541 137,191 8.29 Fiction 13,278 20,380 33,658 212,989 6.33 ZCTC News 8,028 6,479 14,507 151,228 10.42 General prose 19,748 16,389 36,137 354,094 9.80 Academic prose 6,603 705 7,308 141,084 19.31 Fiction 15,103 17,439 32,542 214,042 6.58 5.3 Mean Sentence and Paragraph Length 77

Mean sentence segment length segment sentence Mean 0 News General prose Academic prose Fiction LCMC 7.15 6.89 8.29 6.33 ZCTC 10.42 9.8 19.31 6.58

Fig. 5.9 Mean sentence segment lengths in LCMC and ZCTC

function not the running tokens in text. The latter is usually smaller than the former, because some tokens do not appear in a wordlist (e.g. punctuation marks); furthermore, Arabic numbers are all combined into the same sign “#” and taken as a single type. We use the numbers of “tokens used for wordlist” in order to be consistent with WordSmith which calculates mean sentence length by using the same parameters. Figure 5.9 compares the mean sentence segment lengths of native and translated Chinese texts. Clearly, the mean sentence segment length is greater in translational Chinese than in native Chinese, in all of the four registers, with the most marked contrast in academic prose because the corresponding register in English customarily makes use of long sentences. This fi nding is in line with Wang and Qin’s ( 2010 : 169) observations of literary and non-literary translations. One possible explanation is the so called source language interference, because the mean sentence segment length in English is greater than that in Chinese (the mean sentence segment length is 25.59 words in FLOB but only 13 words in LCMC), given the fact that the correspondent registers in English (e.g. academic prose) tend to use long sentences. In contrast to mean sentence segment length, the mean paragraph length in both corpora demonstrates opposite tendencies. Taken as a whole, native Chinese ( LCMC ) has no signifi cant difference with translated Chinese (ZCTC ) (48.42 vs. 48). However, as shown in Fig. 5.10, there are genre variations in the two corpora: In the three registers of news (A–C), general prose (D–H) and academic prose (J), native Chinese has greater mean paragraph length than translated Chinese, whereas in fi ction (K–R) the difference is reversed, i.e. the paragraphs in translated Chinese are averagely longer than native Chinese. 78 5 The Macro-Statistic Features of Translational Chinese

80 70

20 Mean paragrph length 10

0 A-C D-H J K-R LCMC 51.34 52.43 68.73 35.74 ZCTC 46.56 49.45 65.62 39.88

Fig. 5.10 Mean paragraph length in LCMC and ZCTC

5.4 Word Clusters

We will then discuss word clusters in translated and native Chinese. While the list of word clusters of a corpus looks similar to its wordlist, they are different in that the former is a list of word sequences and the latter is a list of single word types in the corpus. Word clusters are fi xed and semi-fi xed formulaic expressions based on collocations, which are also known as “lexical bundles”, “multiword units”, “pre- fabs”, “n-grams” and so on. Scott ( 2009 : 286) observes that “all words have a tendency to cluster together with some others”. Word clusters are purely structurally defi ned on the basis of co-occurrences with no regard to their semantic contents. As introduced in Chap. 4 , they can be computed automatically by using corpus exploration tools such as WordSmith Tools (Scott 2009 ). Generally speaking, the frequency of word clusters tends to drop sharply as their length grows. For example, the frequency of four-word clusters is signifi cantly lower than that of three-word clusters, which are in turn substantially less frequent than two-word clusters. The statistical signifi cance of word clusters is usually measured by their recurring rate, for example, fi ve or ten occurrences in a million words. In the present study, we use the default settings of the WordSmith Tools (version 5.0), that is, a minimum frequency of fi ve. Another useful parameter in computing word clusters is coverage, which measures how widespread a word cluster occurs in a given corpus. It is expressed as a percentage of the number of text samples containing a particular word cluster in the total number of text samples in that corpus. While word clusters may not necessarily be complete in structure or meaning, they are nevertheless of great importance in language studies. Word clusters have recently been investigated in areas such as genre analysis and language teaching 5.4 Word Clusters 79

Table 5.6 Word clusters in LCMC and ZCTC Word clusters LCMC ZCTC LL score Signiﬁ cance 2-word clusters 21,002 23,006 103.44 0.000 3-word clusters 4,015 5,523 248.36 0.000 4-word clusters 580 732 16.58 0.000 5-word clusters 160 197 4.06 0.044 6-word clusters 70 105 7.25 0.007

(e.g. Granger 1998 ; De Cock 1998 , 2000 ; Cortes 2002 ; Biber et al. 2004 ; Biber 2006 ). In translation studies, Baker (2004 ) and Nevalainen (2005 , cited in Mauranen 2007 ) both fi nd that recurring word clusters are more common in translations in comparison with non-translated texts. This fi nding echoes Baroni and Bernardini’s ( 2003 : 379) observations based on their investigation of collocations in translated and native texts, which even differentiate between two types of repetition patterns: […] translated language is repetitive, possibly more repetitive than original language. Yet the two differ in what they tend to repeat: translations show a tendency to repeat structural patterns and strongly topic-dependent sequences, whereas originals show a higher incidence of topic-independent sequences, i.e. the more usual lexicalized collocations in the language. (Baroni and Bernardini 2003 : 379) Word clusters tend to be more commonly used in translated English. Is this also true in Chinese? What similarities and differences do English and Chinese show in their use of word clusters? In this section, we will study word clusters composed of two to six words because clusters comprising more than six words are rare in million- word corpora like FLOB , LCMC and ZCTC. Table 5.6 gives the frequencies of word clusters in the two Chinese corpora together with the results of log-likelihood (LL ) tests. As can be seen, word clusters of all types are much more frequent in the translational corpus ZCTC than in the native corpus LCMC , and the differences are all statistically signifi cant as indicated by their LL scores and signifi cance levels. The higher use of word clusters in the translational corpus is also evidenced by a keyword cluster analysis. Like a word cluster, a keyword cluster is composed of two or more words which co-occur with each other repeatedly. A keyword cluster differs in that it only uses keywords (cf. Scott 2009 : 145). In this study, the native and translational Chinese corpora, LCMC and ZCTC, are used against each other as the reference corpus in keyword extraction. As the keyword cluster analysis shows, for three-to-fi ve-word clusters, 123 clusters are signifi cantly more common in ZCTC as opposed to just one such cluster which is signifi cantly more frequent in LCMC, and for two-to-six-word clusters, 958 clusters are signifi cantly more common in ZCTC in contrast to 59 such clusters which are signifi cantly more frequent in LCMC. The more frequent use of word clusters in translational Chinese is probably a result of the translation process in which translators strive to achieve fl uency; translators are also likely to be under the infl uence of the English source language. As can be seen in Fig. 5.11, which compares the use of two-to-four-word clusters (clus- 80 5 The Macro-Statistic Features of Translational Chinese

30,000 25,000 20,000

15,000

10,000 Frequency 5,000

0 2-word clusters 3-word clusters 4-word clusters LCMC 21,002 4,015 580 ZCTC 23,006 5,523 732 FLOB 26,491 10,052 1,427

Fig. 5.11 Word clusters in Chinese and English

Fig. 5.12 High-frequency 600 word clusters in Chinese 500 and English 522 400 413 300 291 200 100 0 LCMC ZCTC FLOB

ters of more than four words are infrequent and thus excluded in the graph) in the three comparable corpora, all of the three types of word clusters are most frequent in FLOB and least frequent in LCMC , with ZCTC between the two. In addition to their signifi cantly higher frequencies in translational Chinese, word clusters demonstrate two other interesting characteristics. On the one hand, high-frequency word clusters (defi ned here as those accounting for at least 0.01 % of the respective corpus) are more common in Chinese translations. As can be seen in Fig. 5.12 , the number of high-frequency word clusters in ZCTC (a total of 413, including 403 two-word clusters and ten three-word clusters) is greater than that in LCMC (a total of 291, including 287 two-word clusters and four three-word clusters), which is a statistically signifi cant difference (LL = 21.96, 1 d.f., p < 0.001). Given that translated Chinese tends to use high-frequency words ( Xiao 2010b ), it is hardly surprising to fi nd a more frequent use of high-frequency word clusters in ZCTC. While word cluster s in different languages cannot be directly compared against each other qualitatively, it is also of interest to note in Fig. 5.12 that the use 5.4 Word Clusters 81

350 300 250 200 150

Frequency 100 50 0 >50% >30% >20% LCMC 18 35 101 ZCTC 20 65 170 FLOB 44 158 332

Fig. 5.13 Coverage of two-word clusters in Chinese and English

of high-frequency word clusters in the ZCTC corpus of translational Chinese is quantitatively more akin to the English corpus FLOB , which yields 522 instances of high-frequency clusters (498 two-word clusters and 24 three-word clusters). On the other hand, word clusters have a much wider coverage in translated Chinese in comparison with native Chinese, which is possibly a result of the infl u- ence of English (see Figs. 5.13 and 5.14 ). As can be seen in the fi gures, because of the low overall frequencies of two-word clusters with a minimum coverage rate of 50 % (18, 20 and 44 instances in LCMC , ZCTC and FLOB , respectively) and three- word clusters with a minimum coverage rate of 20 % (0, 4 and 13 instances, respectively), their frequencies are quite similar in the three corpora. However, there is a marked contrast in the frequencies of two-word clusters with a minimum coverage rate of 30 % (35, 65 and 158 instances, respectively) and three-word clusters with a minimum coverage rate of 10 % (8, 23 and 90 instances, respectively) in the three corpora. This contrast displays an accelerating tendency as the coverage rate drops: there are 101, 170 and 332 occurrences of two-word clusters with a minimum coverage rate of 20 % and 61, 132 and 425 instances of three-word clusters with a minimum coverage rate of 5 % in the native and translated Chinese corpora and the comparable English corpus, respectively. The higher frequency and wider coverage of word cluster s in translational Chinese suggest that translators demonstrate a higher propensity for striving for fl u- ency than writers of native Chinese texts. Translators are also likely to be under the infl uence of English, the principal source language of the ZCTC corpus. The use of word clusters is linked to style and is therefore likely to vary across genres. Figure 5.15 illustrates the distribution of high-frequency (with a minimum 82 5 The Macro-Statistic Features of Translational Chinese

450 400 350 300 250 200

Frequency 150 100 50 0 >20% 10% >5% LCMC 0 8 61 ZCTC 4 23 132 FLOB 13 90 425

Fig. 5.14 Coverage of three-word clusters in Chinese and English

700

600

500

400

300 Frequency

200

100

0 Fiction News Academic Prose General Prose LCMC 309 228 255 128 ZCTC 428 286 366 237 FLOB 645 400 362 348

Fig. 5.15 High-frequency high-coverage word clusters in Chinese and English 5.4 Word Clusters 83 proportion of 0.01 % in a given corpus) word clusters of high coverage (with a minimum coverage of 20 % for FLOB and 10 % for LCMC and ZCTC) across four broad text categories. As can be seen, in all of the four broad text categories, high- frequency high-coverage word clusters are consistently more common in the translational Chinese corpus ZCTC than in the native Chinese corpus LCMC (LL = 8.02, 3 d.f., p = 0.045), which suggests that contrary to the normalisation hypothesis, translational Chinese is essentially distinct from native Chinese because translations are likely to be infl uenced by the source language, as can be seen from a comparison of ZCTC and FLOB in Fig. 5.15 . With the exception of academic writing, which is the least variable of the four text categories in our corpora (cf. Xiao 2009a ), high-frequency high-coverage word cluster s are even more common in the English corpus FLOB in all other three categories. Figure 5.15 also shows that in both English and Chinese high-frequency high-coverage word clusters are most commonly used in fi ction and are least frequent in nonfi ction. On the other hand, the two languages differ in that the news category makes marginally more frequent use of such word clusters than academic writing in English whereas in both native and translational Chinese, academic writing makes more frequent use of such word cluster s than news. In addition to the difference in coverage of word cluster s in general, there is also a sharp contrast in the highest coverage rate in the two Chinese corpora. For example, for two-word clusters, the highest coverage rate in LCMC is 69.8 % (Ⲵ а “DE one”), whereas the highest coverage rate in ZCTC is 79.8 % (н ᱟ “not be”); similarly for three-word clusters, the highest coverage rate in LCMC is 19.6 % (ᱟ а ⿽ “be one kind”), whereas the highest rate in ZCTC is 27.6 % (㘼 н ᱟ “but not be”). The highest coverage of some clusters of function words in English can be as high as 100 % (e.g. “of the” and “in the” in FLOB ). Apart from the macro-level quantitative analysis above of frequency and coverage, a qualitative analysis of high-frequency word clusters at micro level yields equally interesting fi ndings. Tables 5.7 and 5.8 show the distribution of high- frequency three-word clusters (defi ned here as those with a minimum normalised frequency of 50 instances per million words) and two-word clusters (defi ned as those with a minimum normalised frequency of 300 instances per million words). It should noted that the three-word cluster 㫢 ᔿ 㙣 “bushel” in Table 5.7 is a technical term unknown to the ICTCLAS tagger used to annotate our Chinese corpora, which incorrectly tokenized it as three separate words. In the table, the character string “S A N” is an acronym for “Storage Area Network”. Furthermore, ICTCLAS is a segmentation system based on both rules and statistical models, the results of which are sometimes different from human segmentation. For example, while ањ yige “one CL” is recognised as one word, є њ liang ge “two CL” are divided into two words by ICTCLAS. The reason behind this is the frequency of use, i.e. ањ yige “one CL” is very frequently used in modern Chinese, which makes it look similar to a word; є њ liang ge “two CL” andа ս yi wei “one CL” are not frequent enough to be segmented as words. It is clear that some two-word and three-word clusters, such as those listed as “common word clusters” in Tables 5.7 and 5.8 , are frequently used in both transla- 84 5 The Macro-Statistic Features of Translational Chinese

Table 5.7 High-frequency three-word clusters in LCMC and ZCTC Type Word cluster Transcription English gloss Common word clusters ᱟ а ⿽ shi yi zhong be one kind ᒦ н ᱟ bing bu shi but not be ᱟ н ᱟ shi bu shi be not be Ⲵ а ⿽ de yi zhong DE one kind ᖸ བྷ Ⲵ hen da de very large DE 䘉 ᱟ а zhe shi yi this is one ᴤ ཊ Ⲵ geng duo de more many DE ᱟ ᡁ Ⲵ shi wo de be I DE ᴰ བྷ Ⲵ zui da de most large DE Ⲵ ᛵߥ л de qingkuang xia DE situation under 㘼 н ᱟ er bu shi but not be ᴹ а ⿽ you yi zhong have one kind ᡰ 䈤 Ⲵ suo shuo de SUO say DE Unique in ZCTC ᴰ 䟽㾱 Ⲵ zui zhongyao de most important DE Ҷ Ԇ Ⲵ le ta de ASP he DE ᴰ ྭ Ⲵ zui hao de most good DE ൘ Ԇ Ⲵ zai ta de in he DE 㫢 ᔿ 㙣 pu shi er transliteration of “bushel” 䘉 Ԧ һ zhe jian shi this CL matter 䘉 є њ zhe liang ge this two CL Ⲵ а 䜘࠶ de yi bufen DE one part Ҷ а ⿽ le yi zhong ASP one kind ઼ Ԇ Ⲵ he ta de and he DE 䟽㾱 Ⲵ ᱟ zhongyao de shi important DE be Ⲵ є њ de liang ge DE two CL ᴤ བྷ Ⲵ geng da de more large DE ᱟ а ս shi yi wei be one CL ᴹ а ཙ you yi tian have one day 㺘䶒 ⍫ᙗ ࡲ biaomian huoxing ji surface active agent S A N SAN S A N ҏ н Պ ye bu hui also not will а ⇥ ᰦ䰤 yi duan shijian one length time ൘ ᡁ Ⲵ zai wo de in I DE Ⲵ suo zuo de SUO do DE ڊ ᡰ Ҷ ྩ Ⲵ le ta de ASP she DE ᴹ є њ you liang ge have two CL ൘ ྩ Ⲵ zai ta de in she DE Unique in LCMC Ⲵ ส⹰ к de jichu shang DE basis on 5.4 Word Clusters 85

Table 5.8 High-frequency two-word clusters in LCMC and ZCTC Type Word cluster Transcription English gloss Common word clusters н ᱟ bu shi not be Ԇ Ⲵ ta de he DE Ҷ а le yi ASP one а ⿽ yi zhong one kind Ⲵ Ӫ de ren DE person Ⲵ а de yi DE one 䘉 ᱟ zhe shi this be н 㜭 bu neng not can 㠚ᐡ Ⲵ ziji de self DE ቡ ᱟ jiu shi precisely be 䘉 а zhe yi this one ѝ Ⲵ zhong de middle DE ᡁ Ⲵ wo de I DE Unique in ZCTC ᱟ а shi yi be one к Ⲵ shang de above DE є њ liang ge two CL Ӫ Ⲵ ren de person DE 䜭 ᱟ dou shi all be ҏ ᱟ ye shi also be ҏ н ye bu also not а ⅑ yi ci one CL ᴹ а you yi have one а ս yi wei one CL ᒦ н bing bu but not de shihou DE time ىⲴ ᰦ བྷ Ⲵ da de large DE ᯠ Ⲵ xin de new DE ྩ Ⲵ ta de she DE ᱟ ൘ shi zai be on ᱟ а shi yi be one ֐ Ⲵ ni de you DE н Պ bu hui not will ԆԜ Ⲵ tamen de they DE Ҷ Ԇ le ta ASP he ᱟ ањ shi yige be one CL ޜਨ Ⲵ gongsi de company DE Ⲵ 䰞仈 de wenti DE issue 䟽㾱 Ⲵ zhongyao de important DE Ҷ ᡁ le wo ASP I Ⲵ 䈍 de hua DE word (“if ”) ᱟ њ shi ge be CL Ԇ 䈤 ta shuo he say ཊ Ⲵ duo de many DE (continued) 86 5 The Macro-Statistic Features of Translational Chinese

Table 5.8 (continued) Type Word cluster Transcription English gloss 䘉ṧ Ⲵ zheyang de this DE ᆳ Ⲵ ta de it DE Ⲵ ањ de yige DE one 䈤 ᡁ shuo wo say I ᡁԜ Ⲵ women de we DE Unique in LCMC Ⲵ ᱟ de shi DE be ࡠ Ҷ dao le arrive ASP Ⲵ ਁኅ de fazhan DE development

tional and native Chinese texts. On the other hand, however, there are a much greater number of high-frequency word clusters which are unique in ZCTC (24 high- frequency three-word clusters and 19 high-frequency two-word clusters) than those which are unique in LCMC (one high-frequency three-word cluster and three high- frequency two-word clusters). Furthermore, it is of interest to note in Tables 5.7 and 5.8 that high-frequency two-word and three-word clusters unique in ZCTC are mostly demonstrative structures (e.g. Ҷ Ԇ Ⲵ le ta de “ASP he DE”, ֐ Ⲵ ni de “you DE” and 䘉ṧ Ⲵ zheyang de “this DE”) and modifying structures (e.g. ᴰ 䟽㾱 Ⲵ zui zhongyao de “most important DE” and ޜਨ Ⲵ gongsi de “company DE”). Indeed many of these demonstrative structures are also modifying structures. In contrast, high-frequency word clusters which are unique in LCMC appear to be mainly head structures (e.g. Ⲵ ส⹰ к de jichu shang “DE basis on, on the basis of”, and Ⲵ ਁኅ de fazhan “DE development, the development of”). While the comparison of two-word and three- word clusters above has revealed some interesting similarities and differences between native and translated Chinese in terms of their use of recurring formulaic expressions, it does not tell us much about how word clusters can help translators to achieve fl uency. Although short word cluster s such as Ⲵ а “DE one” and н ᱟ “not be” do not appear to contribute to fl uency, longer clusters as exemplifi ed in Table 5.9 show that such structurally defi ned recurring formulaic expressions (which may not necessarily be a complete unit of meaning) are certainly as useful in helping the translator to achieve native- like fl uency in translation as they are in a native speaker’s language production. The analyses of word clusters in English and Chinese at the macro and micro levels suggest that word clusters are more prevalent in English than in Chinese. The quantitative and qualitative differences between native and translational Chinese in terms of their use of word clusters suggest that translational Chinese is at best a special variant of Chinese distinct from native Chinese. They also highlight the “relatively higher level of homogeneity of translated texts” in terms of word cluster use (Laviosa 2002 : 72), thus providing fresh evidence in support of the TUs hypothesis of convergence or levelling out . 5.4 Word Clusters 87

Table 5.9 Some examples of three-to-six-word clusters Type Word cluster Transcription English gloss 3-word clusters ц⭼ к ᴰ shijie shang zui most… in the world ѫ㾱 ᱟ ഐѪ zhuyao shi yinwei mainly because а⛩ ҏ н yidian ye bu not at all ᱟ нਟ䚯ݽ Ⲵ shi buke bimian de is unavoidable 4-word clusters ᖸ བྷ 〻ᓖ к hen da chengdu shang to a large extent ᱟ ਟԕ ⨶䀓 Ⲵ shi keyi lijie de is understandable 5-word clusters Ӿ Ḁ⿽ ᜿ѹ к 䈤 cong mouzhong yiyi in a sense shang shuo 䘉 ᒦ н ᱟ 䈤 zhe bing bushi shuo this does not mean 6-word clusters а 䙽 ৸ а 䙽 ൠ yi bian you yi bian di again and again ᴰ ᕪ࣢ Ⲵ ໎䮯 ᱟ ൘ zui qiangjing de the strongest growth is in zengzhang shi zai

The above sections in this chapter have given us an overall picture of the macro- statistic features of translational Chinese based on contrastive analyses of translated and native Chinese corpora. We will discuss in Chap. 8 the implications that these features have for translation universals hypotheses. What follow this chapter are detailed descriptive analyses of the lexical and grammatical features of translational Chinese. Chapter 6 The Lexical Features of Translational Chinese

As we note in the previous chapter, translational Chinese has shown some statistically distinct lexical features from non-translational Chinese as exempliﬁ ed by lower lexical density (namely, smaller proportion of content word s in relation to function word s, which is usually taken as an indicator of the textual information load of a text or corpus), repetitive use of high-frequency word s and extremely low- frequency word s, preference for longer words, quantitative and qualitative differences in word clusters, etc. This chapter will demonstrate in greater detail the speciﬁ c lexical features of translational Chinese ranging from part-of-speech distribution, keywords and key word class es to case studies on pronouns, conjunctions, idioms and reformulation markers.

6.1 Keywords and Key Word Classes in LCMC and ZCTC

As reviewed in Chap. 4 , keywords are an important corpus linguistic means that proves useful in content analysis as well as in stylistic research (Xiao and McEnery 2005b ). In this study, LCMC is used directly as the reference corpus in analysing keywords in ZCTC as the keywords extracted in this way are more characteristic of translational Chinese. Both positive keywords (i.e. those that are exceptionally frequent in the translational corpus) and negative keywords (i.e. those that are signiﬁ - cantly infrequent in the translational corpus) will be included in keyword analysis . Of the 100 most signiﬁ cant keywords in ZCTC (see Appendix 2), nouns that are often mentioned in translated texts take up the largest part (45 in total, e.g. ޜਨ gongsi “company”, 㖾ഭ Meiguo “the USA”, 㖾ݳ meiyuan “US dollar”, 㤡ഭ quanqiu “global”, 㖁㔌 wangluo “network”, ݻ᷇亯 ⨳ޘ ,”Yingguo “Britain guanshui “tariff”), followed 〾ޣ 㖇ᯟ Eluosi “Russia” and״ ,”Kelindun “Clinton by English character strings in addition to some verbs, adverbs and numbers, which

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 89 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_6 90 6 The Lexical Features of Translational Chinese are all required to express the intended ideational meanings. It can be said that these words appear on the keyword list out of the need for expressing the contents. As our study aims to reveal the characteristics of the language used in translational Chinese rather than comparing the contents expressed by non-translational and translational languages, we will focus on the function words on the keyword list, which are deemed to be more useful than content words in studying the language form. Function words on the ZCTC keyword list indicate that translational Chinese makes signifi cantly more frequent use of function words, which is in line with our observation in Chap. 5 . As can be seen in Table 6.1 , pronouns (particularly personal pronouns and demonstrative pronouns ) are more commonly used in translational Chinese. In terms of discourse analysis, personal pronouns and demonstrative pronouns help to achieve textual cohesion and coherence, while conjunctions explicate the logical relationships between clauses. The most prominent function word on the ZCTC keyword list, which is also the third most signifi cant keyword (the fi rst two being content wordsޜਨ gongsi “company” and 㖾ഭ Meiguo “the USA”), is the structural auxiliary Ⲵ de , which is the attributive and possessive modifi er marker in Chinese. This suggests that nouns in translational Chinese are more likely to take attributive modifi ers, thus explaining why average sentence segments in translational Chinese are longer than in native Chinese (cf. Hu 2006 ; see Chap. 3 ). The negative keywords in the translational corpus are mostly related to the contents expressed in non-translational Chinese, e.g. ᡁഭ woguo “our country”, ݳ yuan “Chinese currency unit”, ਼ᘇ tongzhi “comrade”, ᒢ䜘 ganbu “cadre”, ⽮Պ ѫѹ shehuizhuyi “socialism”, ᔪ䇮 jianshe “construction” and 㗔Շ qunzhong “masses”, which rarely appear in translated texts. However, there are also a number of negative keywords that are of interest, e.g. auxiliaries Ҷ -le “a perfective aspect marker ”, ⵰ -zhe “an imperfective aspect marker”, ѻ zhi “a structural auxiliary ” and ㅹ deng “etc.”; pronouns ૡԜ zanmen “we” and க sha “what”; verbs ᴹ you “have”, ᩎ gao “do” and ࣎ ban “do”; as well as conjunctions нն budan “not only” and 㲭 sui “though”. These negative keywords reveal several tendencies in translated texts. First, translational Chinese tends to underuse aspect markers such asҶ -le and ⵰ -zhe (see Sect. 7.8 in Chap. 7 ); second, translational Chinese tends to avoid colloquial (e.g. ૡԜ zanmen “we”), dialectal (க sha “what”) and archaic (ѻ zhi) lexis (see Sect. 7.9); third, translational Chinese may underuse light verbs (e.g. ᩎ gao “do”,

Table 6.1 Key function words in ZCTC Category Keywords (Romanisation and English gloss) Pronoun Ԇ ta “he, him”; ᡁ wo “I, me”; ԆԜ tamen “they, them”; ྩ ta “she, her”; 䘉Ӌ zhexie “these”; 䈕 gai “this”; ᆳ ta “it”; ᡰᴹ suoyou “all”; ֐ ni “you”; 䛓Ӌ naxie “those”; ԫօ renhe “any”; ᡁԜ women “we, us”; ᆳԜ tamen “they, them”; ަԆ qita “other”; 䘉⿽ zhezhong “this kind” Conjunction նᱟ danshi “but”; ྲ᷌ ruguo “if”; ഐѪ yinwei “because”; ᡆ huo “or” Auxiliary Ⲵ de “attributive modiﬁ er marker”; ᡰ suo “nominal marker” Preposition ൘ zai “at, in” 6.1 Keywords and Key Word Classes in LCMC and ZCTC 91

࣎ ban “do”); fi nally, translational Chinese displays a lexical preference that is different from native Chinese which may use near synonyms alternatively for lexical variation (e.g. нӵ bujin “not only” vs. нն budan “not only”, 㲭❦ suiran “though” vs. 㲭 sui “though”), while translational Chinese tends to repeat commonly used words (e.g. нӵ bujin “not only” and 㲭❦ suiran “though”). The analysis of key word classes is very similar to analysing keyword s, but instead of using concrete words, it is based on information about word classes represented by various part-of-speech tags in the annotated corpora; in other words, it is the frequencies of word classes rather than frequencies of word tokens that are used for calculating a list of key word class es. Table 6.2 shows the key word classes and negative key word classes of translated Chinese (ZCTC ) in relation to native Chinese ( LCMC ) sorted in descending order according to their signifi cance level. As can be seen in Table 6.2 , it is not surprising to see that X (non-word character string), NRF (transliterated foreign person name) and NSF (transliterated foreign place name) are overused in translated Chinese. W (symbol and punctuation) is also overused because it is a routine in modern written Chinese to use the dot mark “·” between the fi rst and family names of a transliterated foreign name (e.g. “㓖㘠 · ᯟ 䈪” for “John Snow”). The other key word classes can be put into the following categories: 1 . Pronoun s: e.g. RR (personal pronoun), RZ (deictic pronoun), RZV (verbal pronoun), RG (pronominal morpheme), R (pronoun) and RZS (place pronoun). As mentioned above, personal pronouns and demonstrative pronouns help to achieve textual cohesion and coherence; therefore, the inclusion of pronouns in

Table 6.2 Key word classes in translational Chinese Category Examples (Romanisation and English gloss) Key word X (non-word character string); NRF (transliterated foreign person name); NSF classes (transliterated foreign place name); RR (personal pronoun); W (symbol and punctuation); UDE1 (auxiliary Ⲵ de ); C (conjunction); RZ (deictic pronoun); P (preposition); WKY (closing brackets); WKZ (opening brackets); RZV (verbal pronoun); USUO (auxiliary ᡰ suo ); MQ (numeral classifi er); EW (sentential punctuation); ULS (auxiliary ᶕ䇢 laijiang ; ᶕ䈤 laishuo ; 㘼䀰 eryan ; 䈤ᶕ shuolai ); CC (coordinating conjunction); RG (pronominal morpheme); R (pronoun); RZS (place pronoun); PBEI (preposition㻛 bei ); VSHI (verbᱟ shi ); UYY (auxiliary аṧ yiyang ; а㡜 yiban ; լⲴ shide ; 㡜 ban ); K (suffi x); VX (proverb) Negative NR (Chinese person name); WS (full-length ellipsis); WN (full-length key word enumeration mark); WD (full or half-length comma); NR1 (Chinese surname); classes N (noun); NG (nominal morpheme); UDENG (auxiliary ㅹ deng ; ㅹㅹ dengdeng ; ӁӁ yunyun ); NS (place name); NR2 (Chinese fi rst name); VG (verb morpheme); WM (colon); A (adjective); AD (adjective as adverbial); NL (nominal formulaic expression); VI (intransitive verb); AG (adjectival morpheme); VL (verbal formulaic expression); Z (descriptive word); TG (time word morpheme); NRJ (Japanese name); NZ (other proper noun); S (place word); Y (particle); VF (directional verb); UZHI (auxiliary ѻ zhi ); UZHE (auxiliary ⵰ zhe ); NT (organisation name); VD (adverbial use of verb); VYOU (verb ᴹ you ); T (time word) 92 6 The Lexical Features of Translational Chinese

the key word classes seems to reveal that the translator may intentionally try to improve the cohesion and coherence of translation. 2 . Conjunction s: C (conjunction) and CC (coordinating conjunction) which help to explicate the logical relationships between clauses. 3 . Prepositions (P). 4 . Auxiliaries: e.g. UDE1 (auxiliary, e.g. Ⲵ de), USUO (auxiliary, e.g. ᡰ suo), ULS (auxiliary, e.g. ᶕ䇢 laijiang , ᶕ䈤 laishuo , 㘼䀰 eryan , 䈤ᶕ shuolai ) and UYY (auxiliary, e.g. аṧ yiyang , а㡜 yiban , լⲴ shide , 㡜 ban ). The fi rst four are all function word class es. 5 . Brackets (WKZ and WKY): brackets are normally used in written Chinese to elaborate on the previous word or phrase. 6 . Particular Chinese grammatical patterns : PBEI (preposition㻛 bei ) and VSHI (verbᱟ shi ) (see Chap. 7 for details). 7 . Suffi xes (K): e.g. Ԝ –men , 㘵 –zhe , ⯷ –zheng , ර –xing , ॆ –hua , ⦷ –lv , etc. As a noninfl ectional language, the overuse of suffi xes in translated Chinese may be explicable to source language interference by an infl ectional language such as English. 8 . Proverb (VX): e.g. 䘋㹼 jingxing “proceed”; 㔉Ҹ jiyu “give, award”; and Ҹ ԕ yuyi “give”. The overuse of proverbs in translated Chinese may be taken as infl uenced by the so-called light verb construction, e.g. Sam did the cleaning yesterday. Finally, translated Chinese also tends to overuse numeral classifi er (MQ) and sentential punctuation (EW). 9. Numeral classifi er (MQ). 10. Sentential punctuation (EW). Among the negative key word classe s, it is understandable that Chinese person names and family names are underused signifi cantly in translated texts. The other negative key word classes are mostly nouns (initiated with N in POS tags), verbs (initiated with V), adjectives (A), place word (S), time word (T) and descriptive word (Z). Although there is no unanimous agreement for some of these word class es to be classifi ed as lexical or function words among Chinese grammarians, it is unde- niable that all these word classes are relatively abundant in semantic content. The under-representation of these word classes in translated Chinese is in line with the lower lexical density and reduced textual information load in translated Chinese than in native Chinese discussed in Chap. 5 . What are particularly of interest in these negative word classes are formulaic expressions in nominal forms (NL) and verbal forms (VL) (see Sect. 6.6 for detailed discussion) and VYOU (verbᴹ you “have”) (see Sect. 7.3 in Chap. 7). There are a group of Chinese auxiliaries included in the negative word classes, including UDENG (auxiliary ㅹ deng “etc.”, ㅹㅹ dengdeng “etc.”, ӁӁ yunyun “and so on”), UZHI (auxiliary ѻ zhi), UZHE (auxiliary ⵰ zhe ), particle (Y) and full-length enumeration marks “ǃ” (WN) together with commas, colons and full-length ellipses. As these auxiliaries and punctuation marks are all specifi c to Chinese, the underuse of them are in fact in line with our previous analyses of keywords, which shows that Chinese unique linguistic features appear to be reduced in translational Chinese. 6.2 Word Classes and Word-Class Clusters in LCMC and ZCTC 93

6.2 Word Classes and Word-Class Clusters in LCMC and ZCTC

The analyses of keyword s and key word class es based on translated and non- translated corpora in the previous section have demonstrated various differences in the distribution of word classes . This section will continue to explore the patterns of distribution of word classes and word-class cluster s in both corpora. Figures 6.1 and 6.2 , respectively, compare the distribution of content word classes and major function word classes in LCMC and ZCTC . As the two corpora are of roughly equal size, raw frequency counts are directly comparable. As can be seen in Fig. 6.1, all of the four content word class es are more frequent in non- translational/native Chinese though the difference in adverbs is not as marked as in nouns, verbs and adjectives. Log-likelihood tests (see Table 6.3 ) show that with the exception of adverbs ( LL = 0.10 for 1.d.f., p = 0.757), the differences between native and translational Chinese in other content word classes are all signifi cant (p < 0.001). Figure 6.2 and Table 6.4 show the distribution of major function word classes in the two corpora. Log-likelihood tests indicate that with the exceptions of space words ( LL = 0.55 for 1 d.f., p = 0.458) and non-predicate modifi ers (LL = 0.43 for 1 d.f., p = 0.512), all other word class es display a highly signifi cant difference (p < 0.001). As can be seen in Fig. 6.2 , classifi er s, modal particles, time words, place words and descriptive words are all more commonly used in native Chinese. Descriptive words are similar to adjectives, while place words and time words are often considered as subsets of nouns, which explains why they display similar patterns of distribution to adjectives and nouns, respectively. Given that Chinese is a classifi er language, whereas English is not, with classifi ers used 29 times as frequently in Chinese as in English (see Xiao and McEnery 2010 ), it is unsurprising to fi nd that classifi ers are underused in ZCTC, which contains text samples that are mostly translated from English. Native Chinese makes more frequent use of modal

250,000

200,000

150,000 LCMC 100,000 ZCTC 50,000

0 noun (N) verb (V) adverb (D) adjective (A)

Fig. 6.1 Distribution of content word classes in LCMC and ZCTC 94 6 The Lexical Features of Translational Chinese

90,000 80,000 LCMC ZCTC 70,000

60,000

50,000

40,000

30,000

20,000

10,000

Fig. 6.2 Major function word classes in LCMC and ZCTC

Table 6.3 Content word classes in LCMC and ZCTC Word classes LCMC ZCTC LL score Signiﬁ cance Noun (N) 226,271 209,825 561.40 0.000 Verb (V) 221,920 214,905 88.36 0.000 Adverb (D) 62,853 62,732 0.10 0.757 Adjective (A) 45,065 38,831 441.03 0.000

Table 6.4 Function word classes LCMC and ZCTC Word classes LCMC ZCTC LL score Signifi cance Auxiliary (U) 75,006 85,176 684.04 0.000 Pronoun (R) 49,582 70,401 3707.69 0.000 Preposition (P) 37,744 42,445 293.29 0.000 Numeral (M) 34,535 36,279 49.60 0.000 Conjunction (C) 24,892 31,175 728.84 0.000 Classifi er (Q) 23,186 22,336 12.90 0.000 Space word (F) 18,627 18,416 0.55 0.458 Time word (T) 10,276 9,182 57.59 0.000 Modifi er (B) 9,104 9,159 0.43 0.512 Particle (Y) 6,390 5,455 70.49 0.000 Place word (S) 4,267 3,506 71.85 0.000 Prefi x and suffi x (H/K) 1,840 2,135 23.01 0.000 Descriptive word (Z) 1,712 1,127 119.27 0.000 6.2 Word Classes and Word-Class Clusters in LCMC and ZCTC 95 particle s possibly because they are a unique item in Chinese: modal particles such as ੇ ma , ઒ ne , ୺ a and ੗ ba can hardly fi nd formal equivalents in English (see Sect. 7.10 in Chap. 7). In contrast, auxiliaries, pronouns, prepositions, numerals and conjunctions are all more commonly used in translational Chinese. Given that the frequency of function words is generally higher in translational language, auxiliaries are unsurprisingly more frequently used in translational than non-translational Chinese. Because Chinese is not a morphologically infl ectional language, the more frequent use of prefi xes and suffi xes (e.g. 䎵 chao- “super”, Ԝ -men “plural suffi x”, 㘵 -zhe “-er/-or/-ist”, ᙗ - xing “-ness/-ity”) in translated Chinese texts is arguably a result of the infl uence of English source texts. The above analyses only use information of content and function word classes in translated versus native Chinese texts. We will continue the comparison by making use of word-class clusters and key word-class clusters. The retrieval of word-class clusters is, similar to the retrieval of word clusters, based on the frequencies of co- occurrences of word class es; similarly, word-class clusters are not necessarily complete in meanings and structures. Let us fi rst look at the 3-word-class clusters which have frequencies over 1,000 and coverage rate over 60 % in both corpora. The two corpora are close in absolute counts of these high-frequency 3-word-class clusters, 116 in LCMC and 115 in ZCTC . Tables 6.5 and 6.6 , respectively, show 20 most frequent 3- word-class cluster s in non-translated and translated Chinese corpora. As can be seen in Table 6.5 , while

Table 6.5 20 most frequent 3-word-class clusters in LCMC LCMC rank 3-word-class clusters Examples ZCTC rank 1 N N N ӪབྷᑨငՊѫԫ 5 2 N UDE1 N 呏Ⲵ㗭∋ 1 3 N D V ㅄ㘵䘁ᶕਁ⧠ 2 4 N V N ᖒᔿᴽӾ޵ᇩ 9 5 V N N ޜᐳ䍒࣑⣦ߥ 10 6 N N V ޜᆹ䜘䰘䘭ḕ 11 7 M Q N йњᆙᆀ 8 8 UDE1 N N Ⲵ䈝䀰㹼Ѫ 4 9 D V V ᐢ㓿⵰᡻䈅ࡦ 7 䇱䠁 16؍V V N 㾱Ӕ 10 11 V N V 䇙Ӫ⡡ᙌ 20 12 A UDE1 N ཷᔲⲴᒫᜣ 3 13 N V V 仾࡞䎠 14 14 UDE1 N V Ⲵ༠䈳䈤 6 15 D V N 㓿ᑨ੺䇹⡦⇽ 22 16 N N UDE1 Րཷ㢢ᖙⲴ 18 17 UDE1 N D Ⲵԫ࣑ॱ࠶ 13 18 N N D 㺇ൺ䛫ት䜭 26 19 V UDE1 N নлⲴඳ൮ 12 20 D D V 䘌䘌⋑ᴹ䗮ࡠ 19 96 6 The Lexical Features of Translational Chinese

Table 6.6 20 most frequent 3-word-class clusters in ZCTC ZCTC rank 3-word-class clusters Examples LCMC rank 1 N UDE1 N ᡯᆀⲴൠ൰ 2 2 N D V 㠶䇪аⴤԕѪ 3 3 A UDE1 N ᛺ӪⲴ⎸᚟ 12 4 UDE1 N N ⲴӪ⭏〈䇰 8 5 N N N ᢰᵟઈᐕ䱏Խ 1 6 UDE1 N V Ⲵ౤ᕐᔰ 14 7 D V V ቡՊਁ⧠ 9 8 M Q N йњᆇ 7 N V N 㖾ݳ༴Ҿᕪ࣯ 4 9 10 V N N ᨀ儈Ӫਓ䍘䟿 5 11 N N V བྷ䉶ӗ䟿໎࣐ 6 12 V UDE1 N 䙐ᡀⲴᦏཡ 19 13 UDE1 N D Ⲵ⟳ᯉᐢ 17 ሏઈഎㆄ䈤 13ז N V V 14 15 RR D V ᡁॱ࠶ⴻ䟽 31 16 V V N ᤷ᧗՚䙐ᮠᦞ 10 17 RR UDE1 N ԆⲴ䮯⴨ 35 18 N N UDE1 ᐲ൪ԧṬⲴ 16 D D V ᖃ❦н⴨ؑ 20 19 V N V 䇙Ӫؑᴽ 11 20 generally most 3-word-class clusters made of content word classes, such as noun (N), verb (V) and adverb (D), in non-translated Chinese corpus have higher ranks than in translated Chinese corpus (see word-class clusters like N N N, N V N, V N N, N N V, V V N, V N V, D V N, N N D in Table 6.5), word clusters consisting of auxiliary Ⲵ de “possessive marker” usually have higher ranks in translated corpus than non-translated corpus. In Table 6.6 , high-frequency 3-word-class clusters consisting of function words, including auxiliary Ⲵ de (UDE1) and personal pronoun (RR), usually have higher ranks in translated corpus than in native corpus (e.g. A UDE1 N, UDE1 N V, V UDE1 N, RR D V and RR UDE1 N). By contrast , 3-word-class clusters made of content word class es, noun (N) and verb (V), are generally closer to the list top in LCMC than in ZCTC. Tables 6.7 and 6.8 , respectively, display 20 high-frequency and high-coverage 3-word-class clusters which exist in one corpus but are absent from the other corpus. As can be observed in Table 6.7 , high-frequency and high-coverage 3-word-class clusters present in LCMC but absent from ZCTC are mostly colligations made of content words, among which many are Chinese-specifi c constructions, for example, resultative verb complements (RVCs, e.g. 䎠 ࠪ ഠຳ zou chu kunjing “go out of diffi culty”), inversed numeral-quantifi er structure (e.g. ㌆ 50 ݻ tang 50 ke “sugar 50 grams”) and topic sentence (e.g. ѫ仈 ᜣ ྭ Ҷ zhuti xiang hao le “subject think 6.2 Word Classes and Word-Class Clusters in LCMC and ZCTC 97

Table 6.7 3-word-class LCMC rank 3-word-class clusters Examples clusters in LCMC missing 47 N A N ᛵ┑㓚ᘥา from ZCTC N M Q ㌆ݻ 49 51 N N VN 䇑㇇ᵪᢰᵟᔰਁ 67 N V A ѫ仈ᜣྭҶ 73 V N VN ᣃ㓿⍾ᔪ䇮 78 N V M ᷇ъ໎䮯䫫䘋ڧN V VF ሿ 79 80 N UDE1 A ࣋䟿ⲴՏབྷ 81 F D V ਣ䶒ҏᔰ࿻ 83 A N D ᯠṬተᐢ㓿 ⳶䟼ޕق V N F 89 97 N M N ц⭼䇨ཊഭᇦ 99 N N A 、ᢰ࣋䟿䳴৊ 100 M N N བྷᢩ৽ሩ⍮Ӫ༛ ≥✝D P N ݸ⭘ 104 105 M Q V аս਽ਛ 108 N AD V ᡯӗޜᔰ᣽আ 112 D V M ᐢ䗮з 113 N N M њӪ䍴䠁ӯ 114 V VF N 䎠ࠪഠຳ

Table 6.8 3-word-class ZCTC rank 3-word-class clusters Examples clusters in ZCTC missing 70 P RZ N ൘䛓њᰦᵏ from LCMC 71 RZ Q N Ḁᇦޜਨ 76 P RR V ѪԆ⽸⾧ 84 RZ N V 䈕䇑ࡂᰘ൘ 87 V D A ᝏࡠᖸ᛺䇦 91 RR D D ԆࠐѾн 93 RR V N Ԇ䀓ᔰ亶ᑖ 94 UDE1 M Q Ⲵє⿽ 96 V V RZ ᓄ䈕儈Ҿа࠷ ⲴڊUSUO V UDE1 ᡰ 97 99 UDE1 N RZ ⲴṩⓀѻа 101 D V RZ ᐢᖒᡀḀ⿽ 102 A UDE2 V нᆹൠㅹᖵ ᣔ؍UDE1 N VN Ⲵ⡸ᵳ 106 109 RZ N UDE1 ਴ᯩ䶒Ⲵ 110 N N C ᆖᵟ䇪᮷ᡆ㘵 111 RZ N D 䈕෾ᐲሶ 112 V P RR ࠶㔉Ԇ 114 V V P ᤂ㔍ቸӾҾ 115 P RR UDE1 ޣҾԆⲴ 98 6 The Lexical Features of Translational Chinese good ASP”). In contrast, nearly all high-frequency and high-coverage 3-word-class clusters appearing in ZCTC but missing from LCMC consist of function word s, such as pronouns, prepositions, conjunctions and auxiliaries (e.g. Ⲵ de , ൠ de , ᡰ suo). It is clear that the differences of colligations in translated relative to native Chinese are in line with what has been discussed as for word class es in both corpora, i.e. function words and word clusters consisting of function words tend to be signifi cantly overused in translated Chinese. We will elaborate on this argument with further evidence in terms of key word- class clusters made of 3–8 words. Using the same means of retrieving keywords, we will calculate key word-class clusters by taking the two corpora, ZCTC and LCMC, as reference corpora to each other. We found that 96 % of the fi rst 100 3-word-class clusters in the translated corpus contain various function words like pronouns, prepositions, coordinating conjunctions and auxiliary Ⲵ de ; as a striking contrast, nearly all the fi rst 100 negative key word-class clusters are made of content words like nouns, verbs and adjectives. Table 6.9 shows the fi rst 20 key word-class clusters and 20 negative key word- class clusters made of 3–8 words in ZCTC. As can be seen, the majority of negative key word-class clusters contain nouns, verbs and adjectives. We need to note that the word-class clusters with repeated part-of-speech tags (e.g. M M M, M M M M, M M M M QT, M M N, M M M QT) should not be taken as grammatical items or constructions for that they are due to the technical problems of processing Arabic

Table 6.9 Key word-class Key word-class Negative key clusters and negative key cluster word-class cluster word-class clusters in ZCTC M M M N N N M M M M N V N RR UDE1 N N N N N RR V V N A N RR D V V N N V V RR N M Q C RR V V N V V RR UDE1 N N N N N NS CC NS N N VN M M M M QT V N V N V RR V N N V N RR V RR N N V UDE1 N RR A N A M M N V N N N C RR D N N N N N N D A UDE1 A N V USUO V UDE1 A N N M M M QT N P N N RR V N V N N N C RR N N A 6.3 Distribution of Punctuation Marks in LCMC and ZCTC 99 numbers in texts (spaces between those numbers are used in segmentation, e.g. 1 9 9 0 are segmented into four words instead of one). With the exception of these items, most of the key word-class cluster s consist of function word s and word class es.

6.3 Distribution of Punctuation Marks in LCMC and ZCTC

Punctuation marks can be useful in investigating the properties of translational language. This section will compare the use of major types of punctuation in LCMC and ZCTC . There are two types of punctuation, namely, sentence fi nal (full stop, question mark, semicolon and exclamation) and non-sentence fi nal (all other punctuation marks). Table 6.3 shows the normalised frequencies per 100,000 words of sentence-fi nal and non-sentence-fi nal punctuation (i.e. all other punctuations except for sentence-fi nal punctuation marks) in both corpora. As can be seen, the distribution of punctuation is closely related to registers. In general, general prose (D–H) tends to use the most punctuation, followed by fi ction (K–R) and news (A–C), while academic prose shows the least preference for punctuation. In the same registers, native Chinese (LCMC) and translated Chinese (ZCTC) are very close in use of sentence-fi nal punctuation , but native Chinese shows stronger tendency for non- sentence-fi nal punctuation than translated Chinese in all four registers ( Fig. 6.3 ). However, not all types of punctuation are useful in this research. For example, the frequency of quotation marks and elliptical marks merely indicates the amount of quotations and ellipsis, while the Chinese-style centred dot mark is unavoidable, ؍ instead of as an optional style marker, in translating foreign person names (as in 㖇•ᗧ䴧ݻ, the Chinese transliteration of Paul Drake). Hence, these punctuation marks will not be investigated in this section. A colon is mainly used to introduce a quotation, though it can also function as an explicating device when it is used to

7,000 6,000

5,000

4,000

3,000 Frequency 2,000

1,000

0 LCMC ZCTC LCMC ZCTC LCMC ZCTC LCMC ZCTC (a-c) (a-c) (d-h) (d-h) (j) (j) (k-r) (k-r) Non-sentential punctuation 2,003 1,380 4,635 3,687 1,706 1,199 3,182 2,669 Sentential punctuation 652 645 1,615 1,642 531 554 1,175 1,344

Fig. 6.3 Sentence-fi nal vs. non-sentence-fi nal punctuation in LCMC and ZCTC 100 6 The Lexical Features of Translational Chinese introduce a list of items. But because these two different uses are not separately annotated in our corpora, colons will not be included in our analysis either. Figure 6.4 shows the normalised frequencies (per 100,000 words) of major types of punctuation in native and translational Chinese. Log-likelihood tests show that the differences in the frequencies of all of these punctuation marks are statistically signifi cant (with p = 0.026 for question marks and p < 0.001 for all other punctuation marks; see Table 6.10 ). As can be seen, of the four sentence-fi nal punctuation marks, exclamations, question marks and semicolons are more frequent in native Chinese, whereas full stops are more frequent in translational Chinese. This is presumably because in native Chinese texts, complete sentences do not always end with full stops, because commas are often used to replace full stops, whereas full stops in English source texts tend to be transferred into translated Chinese texts, which explains why full stops are less frequent but commas are more common in LCMC.

8,000 7,000 6,000 5,000 4,000 3,000 LCMC 2,000 ZCTC 1,000 0

Fig. 6.4 Major punctuation marks in LCMC and ZCTC

Table 6.10 Major punctuation marks in LCMC and ZCTC LCMC ZCTC LL score Signiﬁ cance Full stop 35,274 39,887 202.29 0.000 Exclamation 2,211 1,129 377.82 0.000 Question mark 3,031 2,916 4.95 0.026 Semicolon 2,071 1,779 28.08 0.000 Comma 75,403 58,219 2555.28 0.000 Pause mark 12,490 5,573 2850.96 0.000 Parenthesis 6,152 8,890 450.41 0.000 Dash 1,182 1,629 63.13 0.000 6.4 Pronouns in LCMC and ZCTC 101

Of the non-sentence-ﬁ nal punctuation mark s, the pause mark in Chinese is used to indicate a pause between words and phrases within a sentence. The pause mark is non-existent in English, which uses the comma, whereas a pause mark is used in Chinese. In English-to-Chinese translation, a translator is very likely to use the comma by following the English source text rather than use a pause mark. Consequently, the frequency of pause marks in LCMC doubles that in ZCTC. In this regard, it can hardly be said that the normalisation hypothesis is tenable in Chinese. The other two major punctuation marks—parentheses and dashes—can both be used to provide explanations or illustrations (as exempliﬁ ed by the use of dashes and parentheses in this sentence). Except for explanatory punctuation marks such as parentheses and dashes which help to explicate the semantic entity of text, there are other grammatical means to improve the explicitation of a translated text. For instance, the devices of enhancing the cohesion and coherence of text may include the use of pronouns and idioms, while the explicitation of logic relation in a translated text can also be realised with connectives and other cohesive devices such as reformulation markers (see Sect. 6.7 in this chapter). They are explicating devices that facilitate semantic explicitation in translation. As such, these types of punctuation can reasonably be expected to occur more frequently in translational Chinese. In the following chapters, we will demonstrate more case studies with regard to these explicitation devices.

6.4 Pronouns in LCMC and ZCTC

As pronouns have the function of making discourse more cohesive as noted earlier, translational language is hypothesised to make more frequent use of pronouns as a manifestation of explicitation . This section will seek to test this hypothesis by exploring the overall distribution of pronouns in LCMC and ZCTC. As can be seen in Fig. 6.5 , which shows their overall distribution, pronoun s are distributed in native and translational Chinese in a similar pattern, with the most frequent use in fi ction, followed by general prose and news, and the least frequent use in academic prose. However, translational Chinese makes more frequent use of pronouns no matter whether the two corpora are taken as a whole or individual registers (particularly general prose and fi ction) are considered. All differences are statistically signifi cant ( p < 0.001) according to the results of log-likelihood tests (see Table 6.11 ). This observation is also consistent with Hu ( 2010 ). Semantically and functionally, pronoun s in modern Chinese can be classifi ed into personal pronoun , demonstrative pronoun and interrogative pronoun . Figure 6.6 shows the distribution of these three types of pronouns, respectively, in native and translated corpora. As can be observed, on the one hand, personal pronouns are the most frequently used type in either native or translated Chinese, followed by demonstrative pronouns and interrogative pronouns; on the other hand, the difference between personal pronouns and demonstrative pronouns in translated Chinese appears much greater than in native Chinese. Furthermore, as in Table 6.12 , the 102 6 The Lexical Features of Translational Chinese

10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 General Academic News Fiction Mean prose prose LCMC 3,506 3,749 3,066 7,108 4,627 ZCTC 4,105 6,356 3,955 9,391 6,446

Fig. 6.5 Pronouns in LCMC and ZCTC

Table 6.11 Log-likelihood test of pronoun distribution in LCMC and ZCTC Registers LCMC ZCTC LL score Signiﬁ cance News (A–C) 6,670 7,857 69.75 0.000 General prose (D–H) 18,528 29,203 2176.34 0.000 Academic prose (J) 5,240 6,988 195.91 0.000 Fiction (K–R) 19,144 26,353 1603.84 0.000 Mean (A–R) 49,582 70,401 3245.3 0.000

4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Personal Demonstrative Interrogative pronouns pronouns pronouns LCMC 2,405 1,884 330 ZCTC 3,649 2,437 336

Fig. 6.6 Three types of pronouns in LCMC and ZCTC 6.4 Pronouns in LCMC and ZCTC 103

Table 6.12 Log-likelihood tests for three types of pronouns LCMC ZCTC LL score Signiﬁ cance Personal pronoun 25,775 39,855 2,782.17 0.000 Deictic pronoun 20,191 26,618 767.12 0.000 Interrogative pronoun 3,537 3,671 0.59 0.441

log- likelihood tests show interrogative pronoun s are not only less frequently used in both LCMC and ZCTC but also have no statistically signifi cant difference between the two corpora. However, what is of great interest is that the frequencies of the other two types of pronoun s, personal and demonstrative pronoun s, in translated corpus (ZCTC) are strikingly higher than those in native corpus (LCMC), and meanwhile the differences are of statistic signifi cance (p < 0.001). The signifi cantly more frequent use of pronouns (especially personal and demonstrative pronouns) can be regarded as an indicator of translational explicitation (cf. Xiao 2012). The relatively lower frequency of pronouns in LCMC than in ZCTC can be accounted for by the fact that in native Chinese, unlike in English, the grammatical subject can be dropped because of its connective discourse function, whereas the subject in the English source text is likely to be transferred to the translated text. This point is well illustrated in example (1a), which is excerpted from A Madman’s Diary by the renowned Chinese writer Lu Xun:

(1a) ᡁ ⴻн㿱Ԇ, ᐢ㓿йॱཊᒤҶ; Ӻཙ [ᡁ] 㿱Ҷ, ㋮⾎࠶ཆ⡭ᘛǄ [ᡁ] ᡽⸕䚃ԕࡽⲴйॱཊᒤ, [ᡁ] ޘᱟਁ᰿, ❦㘼 [ᡁ]享ॱ࠶ሿᗳǄ Wo kanbujian ta, yijing sanshi duo nian le; jintian [wo] jian le, jingshen fenwai shuangkuai. [Wo] cai zhidao yiqian de sanshi duo nian, [wo] quan shi fahun, ran’er [wo] xu shifen xiaoxin. (1b) I have not seen it for over 30 years, so today when I saw it I felt in unusually high spirits. I begin to realise that during the past 30 odd years I have been in the dark; but now I must be extremely careful.

In example (1a), which is originally written in Chinese, the subject pronoun ᡁ wo “I” is dropped after its occurrence in the fi rst sentence. Although the passage comprises more than one sentence, the subject pronoun in the fi rst sentence functions to glue the ensuing discourse in the excerpt together. Because of the cohesive function of pronoun s in Chinese, a competent Chinese speaker would hardly have any diffi culty in understanding the passage. However, if the same message is translated into Chinese from English (1b), the translator is very likely, under the infl uence of the English source text, to include all of the dropped subjects as highlighted and included in the brackets in (1a). This is because English and Chinese have different conventions of using pronoun s: English tends to repeat personal pronoun s, which is dispreferred in Chinese so that where a pronoun is repeated in a text in English, Chinese either drops the pronoun or repeats a noun instead (cf. Liu 1991 : 371). 104 6 The Lexical Features of Translational Chinese

6.5 Connectives in LCMC and ZCTC

As reviewed in Chap. 3, connectives are one of the central topics in translation universal research (see Sect. 3.3.1 ), mainly because of their function as a device for explicitation in translation. Some previous research have noted that English translations tend to have higher frequencies of connectives than native English, while in some Chinese registers, for example, fi ction and popular science, translated Chinese demonstrates a higher preference for connectives over native Chinese. For instance, Chen (2006 ) fi nds that in his Chinese corpus of popular science books translated from English, connectives are signifi cantly more common than in a comparable corpus of original Chinese scientifi c writing; some connectives are also found to be translationally distinctive, i.e. signifi cantly more common in translated texts. Chen ( 2006 ) concludes that connectives are a device for explicitation in English-to-Chinese translation of popular science books. Xiao and Yue (2009 ) also note that connectives are signifi cantly more frequent in translated than native Chinese fi ction. In this section, we will compare the two balanced corpora of translated and native Chinese in terms of their frequency and use of connectives in an attempt to fi nd out whether the observations by Chen ( 2006 ) and Xiao and Yue ( 2009 ) can also be generalised from specifi c genres to Mandarin Chinese in general. Figure 6.7 shows the normalised frequencies of conjunctions in the ZCTC and LCMC corpora. As can be seen, the mean frequency of conjunctions is signifi cantly higher in the translational corpus (306.42 instances per 10,000 tokens) than in the native (243.23) corpus (LL = 723.12 for 1 d.f., p < 0.001). However, a genre-based comparison reveals more subtleties. Genres of imaginative writing (fi ve types of fi ction K–P and humour R) generally demonstrate a signifi cantly more frequent use of conjunctions in translational Chinese, a fi nding which supports Xiao and Yue’s

500 450 400 350 300 250 200 LCMC 150 ZCTC 100 Frequency per 10,000 words 50 0 J P F E L C B R K H A D G N M Mean Genre

Fig. 6.7 Normalised frequencies of conjunctions in ZCTC and LCMC 6.5 Connectives in LCMC and ZCTC 105

( 2009 ) observation of literary translation. On the other hand, while conjunctions are considerably more frequent in most expository genres (categories A–J) in translated Chinese (particularly reports and offi cial documents H and press reportage A), there are also genres in which conjunction s are more common in native Chinese (namely, popular lore F and academic prose J). Xiao and Yue (2009 ) fi nd that a substantially greater variety of frequent conjunctions are used in translated fi ction in comparison with native Chinese fi ction. While this fi nding is supported by ZCTC and LCMC, the two balanced corpora yield even more interesting results. Figure 6.8 shows the comparison of the frequencies of conjunctions of different frequency bands, as measured in terms of their proportion of the total numbers of tokens in their respective corpus of translational/native Chinese. As can be seen, more types of conjunctions of high-frequency bands—i.e. with a proportion greater than 0.10 % (7 and 4 types for ZCTC and LCMC, respectively), 0.05 % (13 and 7 types) and 0.01 % (43 and 39 types)—are used in the translational corpus. There are an equal number of conjunctions (56 types) with a proportion greater than 0.005 % in the translational and native corpora. After this balance point, the native corpus displays a greater number of less frequent conjunctions of the usage band 0.001 % and below. This fi nding further confi rms our earlier observation of the use of high- and low- frequency words in translated Chinese (cf. Sect. 5.2). It also provides evidence that helps to extend the explicitation hypothesis from English to Chinese and to generalise Chen ( 2006 ) and Xiao and Yue’s ( 2009 ) observations from popular science translation and literary translation to the Mandarin language as a whole. While the tendency to use conjunction s more frequently can be taken as a sign of explicitation , a closer comparison of the lists of conjunctions with a proportion of 0.001 % in their respective corpus also sheds some new light on simplifi cation . There are 91 and 99 types of conjunctions of this usage band in ZCTC and LCMC, respectively. Of these, 86 items overlap in the two lists. Five conjunctions that

200 180 160 140 120 100 LCMC

Frequency 80 ZCTC 60 40 20 0

Fig. 6.8 Distribution of conjunctions across frequency bands 106 6 The Lexical Features of Translational Chinese appear on the ZCTC list but not on the LCMC list are all informal, colloquial and simple, i.e. ԕ㠣Ҿ yizhiyu “so…that…”, ᦒਕ䈍䈤 huangjuhuashuo “in other words”, 㲭䈤 suishuo “though”, ᙫⲴᶕ䈤 zongdelaishuo “in short” and аᶕ yilai “fi rst”, which usually have more formal alternatives, e.g. 㲭❦for 㲭䈤 “though” and ᙫѻfor ᙫⲴᶕ䈤 “in a word”. In contrast, the 13 conjunctions that appear on the LCMC list but not on the ZCTC list are typically formal and archaic including ᭵ “hence”, ਟ㿱 “it is thus clear”, 䘋㘼 “and then”, ࣐ѻ “in addition”, പ❦ “admittedly”, 㔗㘼 “afterwards”, 䶎ն “not only”, ❦ “nevertheless” and ቄਾ “thereafter”. This contrast is illustrated by the patterns of distribution of the two groups of conjunctions across genres. As can be seen in Figs. 6.9 and 6.10 , formal conjunction s of the second group are more common in nearly all native Chinese genres (barring popular lore F), whereas informal and simple conjunctions of the fi rst group are more frequent in most translational Chinese genres (with the exceptions of religious writing D, mystery and detective stories L and adventure stories N). These results appear to suggest that, in spite of some genre-based subtleties, translators tend to use simpler forms than those used in native language, thus providing evidence for the simplifi cation hypothesis but against the normalisation hypothesis.

6.6 Idioms in LCMC and ZCTC

As noted by Sinclair ( 1991 ), languages are used under the “idiom principle”, which operates in combination with the “open-choice principle” to mirror the distinction between conventionality and ﬂ exibility in language use. Of the two, the principle of

50 45 40 35 30 LCMC 25 ZCTC 20 15 10

Frequency per 100,000 words 5 0 J P F L E B R C A H K G N D M

Genres Mean

Fig. 6.9 Distribution of formal conjunctions 6.6 Idioms in LCMC and ZCTC 107

16 14 12 10 LCMC 8 ZCTC 6 4 2 Frequency per 100,000 words 0 J P F L E C R B D A N H K G M Genres Mean

Fig. 6.10 Distribution of informal conjunctions

idiom plays the central role in speech and writing, relying heavily on the speaker or writer’s large inventory of prefabricated lexicogrammatical chunks at their disposal. Idioms in a narrow sense, that is, those characterised with a high degree of structural fi xedness and semantic opacity, can be regarded as an “extreme example” of word clusters (Scott 2009 : 286), as “all words have a tendency to cluster together with some others”. The section will discuss the performances of idioms in native and translated Chinese. While idioms are pervasive in language use, there is unfortunately no universally agreed defi nition of the term. Baker ( 2007 : 14) cites the following defi nition from the Oxford English Dictionary (1989) also cited by Fernando (1996 : 95) which she thinks is adequate: “A form of expression, grammatical construction, phrase, etc., peculiar to a language; a peculiarity of phraseology approved by the usage of the language, and often having signifi cance other than its grammatical or logical one”. In practice, what Baker ( 2004 , 2007 ) studies are “pre-packaged, recurring stretches of language”, which might as well be used as a more operable defi nition of the term. Idioms in Chinese are a complicated category commonly known as ⟏䈝 shuyu (“familiar expression”). They refer to fused phrases or expressions recurring in language use such as ᡀ䈝 chengyu (“idiomatic expression”, typically composed of four Chinese characters), Ґ䈝 xiyu or xiyongyu (“conventional expression”), ᜟ⭘䈝 .(”guanyongyu (“habitually used expression”) and ؇䈝 suyu (“common saying Although the Chinese term chengyu is often translated as “idiom” in English, it only refers to a type of narrow-sense idioms in Chinese. As noted by Yu and Xu ( 2005 ), there are different understandings of these terms. Chengyu are conventionally used set phrases, which are historically allusive in origin, often highly fi xed in structure (i.e. the four-character mould), usually opaque in meaning and typically archaic in style (see Wu 1995 for a review of various defi nitions of chengyu ). In relation to chengyu, fi xed or semi-fi xed phrases and expressions which are highly frequent in language use but have a short history and are thus not historically allusive are often 108 6 The Lexical Features of Translational Chinese called xiyu, which can equally opaque in meaning (cf . An et al. 2004 ). Guanyongyu (“habitually used expression”) refer to recurring fused phrases or expressions which are usually transparent in meaning, while suyu (“common saying”) are similar except that they are more colloquial in style. Except the narrow-sense idioms chengyu , Chinese shusu (“familiar expression”) of other types discussed here are broad-sense idiom s that vary in structural fi xedness, in semantic opacity as well as in style. According to Fernando (1996 ), idioms can be pure, semi- or literal idioms in terms of their idiomaticity, while Halliday ( 2004 ) classifi es idioms into ideational, interpersonal and relational types on the basis of their functions. Clearly, although idioms can only occur as one sentential constituent because of their holistic form and meaning, they can nevertheless play a number of roles in discourse and have numerous discourse functions (cf. Fernando 1996 ). Such complexities, coupled with the pervasiveness and cultural specifi city of idioms as well as the cultural diversity associated with language use, constitute a challenge in translation which translators must cope with if they are to translate idioms in the source language into appropriate idioms in the target language . In spite of their importance, however, idioms seem to have rarely been studied in translation research, with the exception of Baker (2007 ). Baker (2007 ) studies the use of idioms in translated English in comparison with native English on the basis of the fi ction and biography components of the Translational English Corpus (TEC; see Baker 2004 ) and a comparable set of fi ction samples from the British National Corpus (BNC; see Aston and Burnard 1998). Baker ( 2007 : 14) assumes, on the basis of the normalisation hypothesis, that “translators are likely to opt for safe, typical patterns of the target language and shy away from creative or playful uses”, and consequently, “translators ought to be making heavy use of idiom s, in the broad sense of pre-packed, recurring stretches of language”. On the other hand, as idioms, especially those which are highly opaque in meaning (e.g. chew the fat ), “tend to be highly informal in fl avour”, they are therefore expected to be avoided in translations, which “generally tend to be characterised by a higher level of formality than non-translations”. These observations point in two opposite directions. On the one hand, translations are expected to make heavier use of idioms to conform to the target- language norm, while on the other hand, idioms, and opaque idioms in particular, are expected to be avoided in translations because of their informal fl avour. Unfortunately, Baker ( 2007 ) only gives some examples (off the hook , out of order ) to show that opaque idioms are more likely to be avoided in translations than in non- translated texts but do not provide statistics of the overall proportions of literal and opaque idioms in the translational versus native English data. It is clear from the above discussion that idioms in Chinese are very different from English idioms in structure and etymological sources. As idioms are culturally rooted, they also embody different cultural traits such as historical backgrounds, natural environments, religious beliefs and world views (cf. Yang 2004). For example, English idioms, particularly idioms with metaphorical meanings, tend to be used as slangs, while Chinese idioms (especially chengyu “idiomatic expression”) normally are archaic in structure and quite different from their literal meanings. Nevertheless, idioms in English and Chinese are similar to each other in that they 6.6 Idioms in LCMC and ZCTC 109 are both pre-packed, recurring formulaic expressions that help to achieve idiomaticity in their respective language. Then, what can we fi nd in the use of idioms (in its broad sense) in translated Chinese in relation to its native counterpart? Before we start a detailed analysis of idiom s, it should be noted that in the LCMC and ZCTC corpora, idioms are tagged according to their word class es: nl for nominal idioms, vl for verbal idioms, al for adjectival idioms, dl for adverbial idioms and bl for nominal modifying idioms (see Appendix 1 for part-of-speech tag set of both corpora). These annotations facilitate the following analysis and comparison. Figure 6.11 shows the normalised frequencies (per 100,000 words) of idioms across the four broad registers covered in the LCMC corpus of native Chinese and the ZCTC corpus of translated Chinese. As can be seen, in all four registers, idioms in the native corpus LCMC are considerably higher than in the translational corpus ZCTC. Table 6.13 shows the raw frequencies of idioms in the two corpora, which are used in log-likelihood (LL ) tests to measure the statistical signifi cance of the difference in the frequencies between the two corpora. The table also shows that when the two corpora are taken as a whole, the overall frequency of idiom s in LCMC (7,979 occurrences) is signifi cantly higher than that in ZCTC (6,265 occurrences), with an LL score of 196.81 and a signifi cance level less than 0.001 (Table 6.13 ). Figure 6.12 and Table 6.14 show more detailed analyses of the frequencies of idiom s across the 15 genres in both corpora. As the normalised frequencies (per 100,000 words) of idioms across the 15 genres show, with a few exceptions (e.g. E, L and N), idioms in the native corpus LCMC are considerably higher than in the translational corpus ZCTC. Table 6.14 shows the raw frequencies of idioms in the two corpora, which are used in log-likelihood (LL ) tests to measure the statistical signifi cance of the difference in the frequencies between the two corpora. Baker ( 2007 : 14) assumes, on the basis of the normalisation hypothesis, that “translators are likely to opt for safe, typical patterns of the target language and shy away from creative or playful uses”, and consequently, translators tend to make heavy use of “pre-packed, recurring stretches of language” or what she refers to as “ idiom s”. On the other hand, Baker ( 2007 ) also expects such formulaic expressions to be avoided in translations because of their often informal fl avour arising from

Fig. 6.11 Normalised 1,200 frequencies of idioms across registers 1,000 800 600 400 200 0 A-C D-H J K-R Mean LCMC 998 767 610 656 737 ZCTC 661 547 482 597 569 110 6 The Lexical Features of Translational Chinese

Table 6.13 Frequencies of idioms across registers Register LCMC ZCTC LL score Signiﬁ cance News (A–C) 1,884 998 130.46 0.000 General prose (D–H) 3,233 2,468 112.80 0.000 Academic prose (J) 1,051 858 26.09 0.000 Fiction (K–R) 1,811 1,663 7.61 0.006 Mean (A–R) 7,979 6,265 196.81 0.000

1400

1200

1000

800

600 LCMC 400 ZCTC

Frequencies per 100,000 words 200

0 J P F L E C R B D H A K G N M Mean Genre

Fig. 6.12 Normalised frequencies of idioms across genres their highly opaque meaning (e.g. “chew the fat”). Clearly, our Chinese data supports the second tendency, whereas it does not conform to the normalisation hypothesis in terms of idiom usage.

6.7 Reformulation Markers in LCMC and ZCTC

A particular type of idiom s or word clusters in Baker (2004 , 2007 ) is the so-called reformulation markers such as in other words and that is to say , though a reformulation marker can also be a single word instead of a word cluster (e.g. namely ). Reformulation markers are a kind of discourse markers which function to enhance connectivity in discourse (Schourup 1999 : 230). Murillo (2004 : 2066) calls them “markers of the explicit” as these discourse markers “assist, to varying degrees, in the inferential process by making explicit reference assignment, disambiguation, further enrichment and elliptic material in connection with the recovery of the 6.7 Reformulation Markers in LCMC and ZCTC 111

Table 6.14 Frequencies of idioms across genres Genre LCMC ZCTC LL score Signifi cance A 809 499 72.37 0.000 B 632 457 27.23 0.000 C 443 320 19.20 0.000 D 209 135 15.61 0.000 E 368 405 2.00 0.158 F 794 666 10.50 0.001 G 1,272 1,141 6.37 0.012 H 590 121 334.23 0.000 J 1,051 858 18.43 0.000 K 398 355 2.21 0.137 L 315 374 5.41 0.020 M 72 57 1.66 0.197 N 412 447 1.64 0.200 P 475 354 17.02 0.000 R 139 76 18.37 0.000 Total 7,979 6,265 196.81 0.000 propositional form”. Murillo (2004 ) observes, from the viewpoint of relevance theory (Sperber and Deirdre 1995 ), that reformulation markers not only function to recover the propositional form of an utterance, but they also operate in relation to its explicatures and implicatures “by explicitating implicated premises and conclusions” ( 2004 : 2066). There are different opinions in the literature about what counts as a reformulation marker. The term used in a narrow sense refers to discourse markers which are strictly paraphrastic, i.e. indicating equivalence (e.g. Murillo 2004 ). On the other hand, as Cuenca (2003 : 1072) observes, “Reformulation is more than a strict paraphrase”. She argues that “[i]t should be considered a complex semantic category that ranges from strict paraphrase to other values such as specifi cation, explanation, summary or denomination, and even to non-paraphrastic meanings such as implication, conclusion and contrast” (Cuenca 2003 : 1073), though only paraphrastic reformulation markers are investigated in her study. While “nonparaphrastic reformulations typically recapitulate or resume something from the preceding discourse”, paraphrastic reformulation markers have “the metalinguistic function of clarifying, spec- ifying, expanding or elaborating without changing the semantic content” ( Aijmer 2007a : 44–45), although according to Del Saz and Fraser (2005 ), the distinction between paraphrastic versus nonparaphrastic is diffi cult to maintain, at least for English. Instead, they classify reformulations in English into four categories: “expansion” (i.e. providing more information), “compression” (i.e. summarising or reca- pitulating with a single expression), “modifi cation” (i.e. modifying a prior segment) and “reassessment” (i.e. revising the speaker’s opinion of an implication conveyed by a prior segment). Blakemore ( 2007 ) provides a nice discussion of the varieties of reformulations as well as various approaches to classifying reformulation markers. 112 6 The Lexical Features of Translational Chinese

While the glossing and explicating functions of reformulation markers render them particularly relevant to the explicitation hypothesis in translation universal research, in contrast to theoretical explorations, the research of reformulation markers in translation studies has just begun (e.g. Olohan and Baker 2000 , Olohan 2004, Baker 2004 ; Mutesayire 2005 ). As a beginning of these studies, Baker’s (2004 ) study on reformulation markers such as that is , that is to say and in other words in translated English sets up an initial example. In a survey of the distribution of reformulation markers in translational English, Baer ( 2004 : 175–176) proposes a hypothesis: if the translator does have a preference for more fl uent translation, this preference can be represented by those high-frequency linguistic features, among which reformulation markers are one. The use of reformulation markers in texts may partly refl ect the pragmatic and discourse organising strategies and ultimately the pragmatic-cognitive awareness of the author or translator. According to Baker (2004 ), the high-frequency use of reformulation markers is in fact a strategy of “prioritizing fl uency in translation”. By comparing translational and native corpora, Baker (2004 ) fi nds that reformulation markers are substantially more frequent in the fi ction and biography components of the Translational English Corpus than in the fi ction subcorpus of the British National Corpus : that is (288 vs. 19), in other words (161 vs. 36) and that is to say (129 vs. 31). Mutesayire (2005 ) offers a similar study to Baker (2004 ) in supplying additional evidence for reformulation markers such as in other words , namely , that is to say, etc., being explicated in translational English in relation to native English. Chen (2006 ) studies the connective s in translated and native Chinese. One part of his research discusses the explicitation of reformulation markers based on a parallel corpus consisting of English source texts with two Chinese translations (one from mainland China, the other from Taiwan) under the subject of popular science and a comparable corpus of native Chinese (a scientifi c and technological subcorpus of the Sinica corpus) (see Sect. 4.1 in Chap. 4). According to Chen (2006 ), reformulation marker s in Chinese, which are mainly placed at the beginning or in the middle of a sentence, have various expressive functions such as showing attitude, estimating, implying, notifying and exemplifying. Chen ( 2006 ) fi nds that in contrast to the native Chinese corpus (SC-SCI), both the Taiwan translated corpus (ECPC-TW) and the mainland translated corpus (ECPC-CN) show signifi cantly higher frequencies of reformulation marker s than native Chinese of the same subject area (see Table 6.15 ). Chen’s ( 2006 ) research supports the hypothetical explicitation of reformulation marker s in translated Chinese texts. However, as noted by Biber ( 1995 : 278), the differences between various genres in the same language may be more signifi cant than the differences between languages. Xiao ( 2009a ) also shows academic register is the least variant register in English, which suggests Chen’s ( 2006 ) fi ndings may be due to stylistic similarities rather than caused by the transfer from English to Chinese. As discussed previously in Sect. 6.5 of this chapter, we have found that translation Chinese tends to prefer use of informal, oral and simple connective s, while native Chinese prefers formal, literate and even archaic connectives. Then, as a type 6.7 Reformulation Markers in LCMC and ZCTC 113

Table 6.15 Commonly used reformulation markers in Chinese (Chen 2006 : 152) Reformulation marker Romanisation Gloss ECPC-TW ECPC-CN SC-SCI ҏቡᱟ䈤 Ye jiu shi shuo That is to say 103 53 39 ᦒਕ䈍䈤 Huan ju hua shuo In other words 66 35 20 ᙫ㘼䀰ѻ Zong er yan zhi In summary 7 4 5 ㆰ䀰ѻ Jian yan zhi Simply put 10 3 4 ᱃䀰ѻ Yi yan zhi In other words 6 0 1 ㆰ㘼䀰ѻ Jian er yan zhi In simple words 5 2 0 ᙫⲴᶕ䈤 Zong de shuo lai Generally 0 5 0 speaking ᖂṩࡠᓅ Gui gen dao di Ultimately 0 3 0 н⭘䈤 Bu yong shuo Needless to say 5 11 1 Total 202 116 70

Table 6.16 Literate and oral reformulation markers in LCMC and ZCTC Reformulation Style marker Romanisation Gloss LCMC ZCTC Literate ণ Ji Namely, i.e. 267 274 ᦒ䀰ѻ Huan yan zhi To put it differently 5 8 Oral ҏቡᱟ䈤 Ye jiushi shuo That is to say 27 28 ᡆ㘵䈤 Huozhe shuo Or rather 14 25 ᦒਕ䈍䈤ᦒਕ䈍 Huan ju hua (shuo) Put in other words 8 18 䇢䘉ቡᱟ䈤 Zhe jiushi shuo That is to say 15 10 䘉ቡ᜿ણ⵰ Zhe jiu yiwei zhe This means… 1 20 ᡁⲴ᜿ᙍᱟ Wode yisi shi What I mean is… 1 10 ᴤ⺞࠷߶⺞ާ Geng queqie/zhunque/ To be more precise 2 7 փൠ䈤 juti de shuo Total 340 400 of connectives, do the same tendencies present as well as in our balanced comparable corpora? The present section compares the use of reformulation markers in native and translational Chinese as well as in native and translational English. Reformulation markers in Chinese and English will fi rst be discussed separately and then the results will be contrasted. Finally the explicitation of reformulation marker s in English-to- Chinese translation will be investigated. First of all, it should be noted that because our corpora are not annotated in the semantic properties of discourse reformulation markers, the following study only focuses on commonly used paraphrastic reformulation markers for elaboration and explicitation in Chinese as listed in Table 6.16 . The list was compiled on the basis of the author’s knowledge about Mandarin Chinese as a competent native speaker. While the list may not be exhaustive, it covers some most commonly used reformulation markers in Chinese. Syntactically, some of them can be used either as 114 6 The Lexical Features of Translational Chinese a connective or as a predicate. As we are only interested in those instances which are used as reformulation connectives, all instances of these items were retrieved from the two Chinese corpora and evaluated manually in KWIC (keyword in context) concordances to avoid errors in automatic annotation of word class es. Table 6.16 shows the frequencies of reformulation markers in the two Chinese corpora following the human analysis. Table 6.16 shows that the translational corpus ZCTC makes more frequent use of reformulation marker s than the matching native Chinese corpus LCMC, and the difference in the overall frequencies in the two corpora is statistically signifi cant ( LL = 4.52, 1 d.f., p = 0.033). It is even more interesting to note the contrast between reformulation markers of different styles, which are labelled as “literate” and “oral” reformulation markers, following Biber’s (1988 ) terms. The two literate reformulation markers ণ ji “namely, i.e.” and ᦒ䀰ѻ huan yan zhi “to put it differently” have an archaic fl avour, but they are no longer viewed as archaisms because of their prevalence in modern Chinese discourse, especially in formal writing. Although they are terse in form, these reformulation markers are not stylistically simpler than the more colloquial forms such as ҏቡᱟ䈤 ye jiushi shuo “that is to say” and ᦒਕ 䈍䈤 huan ju hua shuo “put in other words”, which are referred to as oral reformulation marker s in this study. As can be seen in Fig. 6.13 , although both literate and oral reformulation marker s are more common in the translational corpus ZCTC , the frequencies of the literate forms of reformulation marker s in the two corpora are very close and the difference is not signifi cant ( LL = 0.127, 1 d.f., p = 0.772), whereas the oral forms are substantially more common in translational than native Chinese (LL = 13.31, 1 d.f., p < 0.001). This fi nding is in line with the distribution patterns of formal and informal conjunctions in native and translational Chinese as observed in Xiao and Yue (2009 ). For easy manipulation of data and comparison with English, we will regroup the 15 genres in our corpora into two broad text categories: literature (K–R) and

300

250

200

150 LCMC ZCTC Frequency 100

0 Literate Oral

Fig. 6.13 Literate and oral reformulation markers in LCMC and ZCTC 6.7 Reformulation Markers in LCMC and ZCTC 115

non- literary (A–J). Table 6.17 shows the frequencies of literate and oral reformulation markers in native and translational Chinese in these two broad categories. It can be seen that in literature, literate reformulation markers are more common in native Chinese, whereas oral reformulation marker s are more frequent in translated Chinese. In nonliterature, while both native and translated Chinese varieties make more frequent use of literate reformulation markers , oral reformulation marker s display a greater proportion in translated Chinese (25 %) than native Chinese (18 %). This means that translational Chinese differs quantitatively from native Chinese in its use of reformulation marker s, especially the oral forms. The different behaviours of literate and oral reformulation marker s are more clearly illustrated in their distribution across genres in native and translational Chinese. As can be seen in Fig. 6.14 , although the overall frequencies of literate reformulation markers in the two corpora do not differ signifi cantly, there are considerable variations across genres. Literate reformulation markers are infrequent in imaginative genres of literature (K–R) in both native and translational Chinese corpora. In infor- mative genres of non-literary (A–J), they are signifi cantly more common in news (A–C), biographies and essays (G) and reports/offi cial documents (H) in translated texts, but much more frequent in religious writing (D), popular lore (F) and academic prose (J) in native Chinese texts. These three genres of the second group are all formal types of writing which demonstrate a high propensity for a formal style.

Table 6.17 Distributions of literate and oral reformulation markers in Chinese Text category Corpus Literate reformulation markers Oral reformulation markers Literature LCMC 26 76 % 8 24 % (K–R) ZCTC 20 42 % 28 58 % Nonliterature LCMC 272 82 % 61 18 % (A–J) ZCTC 277 75 % 91 25 %

90 80 70 60 50 40 LCMC Frequency 30 ZCTC 20 10 0

Genre

Fig. 6.14 Distribution of literate reformulation markers across genres in Chinese 116 6 The Lexical Features of Translational Chinese

In contrast, as can be seen in Fig. 6.15, oral reformulation markers are more frequently used in most genres in translational Chinese. While they are still more frequent in religious writing (D) and popular reading (F) in native Chinese, the contrast between native and translated texts is less marked than that in literate reformulation marker s. These observations suggest that, on the one hand, the use of reformulation markers varies across genres, while on the other hand, translational Chinese has a tendency to use stylistically simpler forms in comparison with native Chinese, thus providing fresh evidence for simpliﬁ cation in translations but serving as a counterexample of the normalisation hypothesis. The remainder of this section will compare the above ﬁ ndings about reformulation markers in translational Chinese with observations of reformulation markers in translational English and explore the explicitation of reformulation marker s in English-to- Chinese translation. As there is no corpus of translational English that is comparable to the FLOB corpus of native English that mirrors the relationship between LCMC and ZCTC for Chinese, we will use the BFSU General Chinese-English Parallel Corpus (GCEPC ), a balanced bidirectional parallel corpus composed of a roughly equal amount of literary and non-literary English and Chinese originals with their respective Chinese and English translations (Wang 2004 ; see Chap. 4 ). The comparable components in this parallel corpus will not only reveal the usage patterns of reformulation markers in native and translational English as well as in literature and nonliterature, but they will also allow us to examine reformulation markers as an explicitation device. Table 6.18 shows a list of commonly used paraphrastic reformulation markers in English, which was established on the basis of a literature review, including all items that have been studied in the research reviewed in this article. As for Chinese, each concordance line containing these reformulation markers was evaluated manually to remove noise. Table 6.19 shows the frequencies of literate and oral reformulation markers in native and translated English texts. It can be seen that in both literature and

15 LCMC 10 Frequency ZCTC

0 Genre

Fig. 6.15 Distribution of oral reformulation markers across genres in Chinese 6.7 Reformulation Markers in LCMC and ZCTC 117

Table 6.18 Commonly used reformulation markers in English Type Examples Oral reformulation markers That is, in other words, that is to say, I mean, this means, that means, it means, which means, this meant, that meant, it meant, which meant, or more generally, to be more precise, to be more accurate, or more precisely, or more accurately, or rather, put differently, or better still, or better yet, or to say the same thing in a different way, to put it simply, to put it concretely Literate reformulation i.e., viz., namely markers

Table 6.19 Distribution of oral and literate reformulation markers in English Literate reformulation Oral reformulation Text category Corpus markers markers Literature Native English 0 0 % 17 100 % Translational English 2 10 % 19 90 % Nonliterature Native English 5 17 % 25 83 % Translation English 26 26 % 75 74 %

nonliterature, oral reformulation marker s are more frequent than literate forms, and both oral and literate forms are more commonly used in translational than native English, though the differences in the literature component are marginal. A comparative analysis of the distribution of the two types of reformulation markers in native and translational English with that in native and translational Chinese (Tables 6.17 and 6.19) suggests that oral reformulation markers are more common than their literate counterparts in both native and translational English and in both literature and nonliterature, possibly because two out of the three literate forms (“viz.” and “i.e.”) in this study are archaic and are largely used only in very formal writing. In contrast, literate reformulation markers are more common than oral forms in Chinese, with the exception of translated literature, which makes more frequent use of oral reformulation markers possibly to simplify the translated language. In relation to native English, both literate and oral reformulation markers are more commonly used in translational English, with an even more marked contrast in translated nonliterature. The same is true of Chinese except for the use of literate reformulation marker s in translated literature, but the contrast between native and translational Chinese varieties is less marked than their English counterparts. In terms of the two broad text categories, reformulation markers are more common in non-literary than literature in both English and Chinese, but in this case, the contrast is more marked in Chinese than in English. We noted earlier that paraphrastic reformulation marker s are an explicative device in translation. Then to what extent are reformulation markers explicated in the translation process (that is, they are used in Chinese translations but not in English source texts)? To answer this question, we searched the English-to- Chinese translations of literature and non-literary components in the BFSU parallel corpus , totalling 814,269 English words and 677,126 Chinese words. 118 6 The Lexical Features of Translational Chinese

Table 6.20 Explicitation of reformulation markers in English-to-Chinese translation Reformulation Transferred Explicated Explicitation marker Romanisation Gloss from ST in TT rate ণ Ji Namely, i.e. 14 82 85.4 % ҏቡᱟ䈤䘉 Ye/zhe jiushi That is to say 11 5 31.3 % ቡᱟ䈤 shuo ᦒ䀰ѻ Huan yan zhi Or rather 3 1 25 % ᡆ㘵䈤 Huozhe shuo Put in other words 15 2 11.8 % ᦒਕ䈍䈤 Huan ju hua That is to say 18 2 10 % shuo ᴤ⺞࠷ާփ Geng queqie/ To be more 7 0 0 ൠ䈤 juti de shuo precise/speciﬁ c ᡁⲴ᜿ᙍᱟ Wo de yisi shi What I mean is… 7 0 0 䘉ቡ᜿ણ⵰ Zhe jiu yiwei This means… 1 0 0 zhe

Table 6.20 shows the frequencies of reformulation markers in Chinese translations which are transferred from the English source texts and those which are supplied in the translation process as well as their explicitation rates. As can be seen, typically 10–30 % of reformulation markers are explicated (i.e. added by translators), with explicitation rates varying markedly from zero to over 85 % in extreme cases. It is also clear that the more semantic content a reformulation marker contains, the less likely it is to be explicated. For example, the highly semantically loaded reformulation markers like ᴤ⺞࠷/ ާփൠ䈤 geng queqie/juti de shuo “to be more precise / speciﬁ c”, ᡁⲴ᜿ᙍᱟ wode yisi shi “what I mean is…” and 䘉ቡ ᜿ણ⵰ zhe jiu yiwei zhe “this means…” are all transferred from the English source texts, whereas the purely paraphrastic and explicative reformulation marker ণ ji “namely, i.e.” has a very high rate of explicitation, as demonstrated in the following examples1 :

(2) After Lewes’ death in 1878, Eliot wrote nothing further, dying just 2 years later, in 1880. 1878ᒤ 䐟᱃ᯟ ↫ ਾ, 㢮⮕⢩ ޽ ⋑ ߉ ӰѸ ь㾯Ǆєᒤਾ, 1878 nian Luyisi si hou, Ailuete zai ye mei xie shenme dongxi. liang nian hou 1878 year Lewes die after, Eliot again not write what stuff. 2 year after ণ 1880ᒤ, 㢮⮕⢩ ҏ ৫ц ҶǄ Ji 1889 nian, Ailuete ye qushi le. i.e. 1880 year Eliot also die ASP

1 These examples are cited from our English-to-Chinese parallel corpus. In the literal glosses of Chinese examples, ASP stands for “aspect marker”, CL for “classiﬁ er”, DE for structural particle de, PSV for “passive”, RVC for “resultative verb complement” and SUO for the particle suo, which is used before a verb to form a nominal construction. 6.7 Reformulation Markers in LCMC and ZCTC 119

(3) The American frontier fostered the notion that “everybody is an entrepreneur” or that everybody has the right to try his hand. 㖾ഭ Ⲵ 㾯䜘䗩⮶ӗ⭏ ࠪа ⿽ 㿲⛩, ণ 䇔Ѫ:⇿њӪ䜭 Meiguo de xibu bianjiang chansheng chu yizhong guandian, ji renwei: meige ren dou US DE west frontier foster RVC one kind viewpoint, i.e. think: everyone all ᱟࡋъᇦ, ᡆ㘵䈤㠣ቁ ਟԕ 䈤 ⇿њ Ӫ 䜭 ᴹᵳ 䈅а䈅Ǆ Shi chuangyejia, huozhe shuo zhishao keyi shuo meige ren dou youquan shiyishi. Be entrepreneur, or at-least may say everyone all have-right try one try

In examples like these, the reformulation marker “namely, i.e.” in Chinese translations (underlined in the examples) cannot fi nd its equivalence in the English originals; in other words, they are supplied by translators as an explicitation strategy. Explicitation of reformulation markers in English-to-Chinese translation typically occurs where appositions are used in the English source texts as in (2), where “just 2 years later” actually refers to the prepositional phrase “in 1880”. Appositions can not only be phrases but also clauses, as exemplifi ed in (3), where the noun phrase “the notion” refers to what is expressed by the two “that” clauses. In addition to appositions in the English source texts, other structures are translated into Chinese using explicated reformulation marker s as well. For example, in (3) the “which” relative clause is translated using a reformulation; in (4) the “by” prepositional phrase is reformulated; in (5) the Chinese translation reformulates what is expressed by the infi nitival complement in the source text (i.e. “to take campaigns out of unregulated hurly-burly of politics”), while Chinese translation uses a totally different sentence structure in (6). In addition to their explicative function in translation, reformulation marker s, especially the semantically less full marker ণ“namely, i.e.”, also function to break long sentences in the English source texts into shorter sentence segments, which are characteristic of Chinese:

(4) This is the full text of the 1989 second edition, which consolidates the original OED all the supplementary volumes and additional new materials. 䜘 ޵ᇩ, ণ ৏ݸ Ⲵޘᆳवᤜ Ҷ 1989ᒤ ㅜҼ ⡸ Ⲵ Ta baokuo le 1989 nian di’er ban de quanbu neirong, ji yuanxian de It include ASP 1989 year second edition DE all content i.e. former DE ⢋⍕ 䗎ިǃ 㺕䚇䜘࠶ ৺ ᯠ ໎ᶀᯉǄ Niujin cidian, buyi bufen ji xinzeng cailiao. Oxford dictionary supplementary part and new add material (5) Dr. Butler made history by being the ﬁ rst woman head of a formerly all-male college… Ҷ ≨඲ਢ޼ Ⲵ һᛵ, ণ ྩ ᱟㅜаսڊ ᐳ⢩䎆 ঊ༛ Butelai boshi zuo-le yongchuishice de shiqing, ji ta shi diyiwei Butler Dr do ASP make-history DE thing i.e. she is ﬁ rst CL ৏ᶕ Ӫઈ ޘ Ѫ⭧Ӫ Ⲵ ᆖ䲒 ѝ ᣵԫ ṑ䮯 Ⲵ ྣᙗǄ 120 6 The Lexical Features of Translational Chinese

Yuanlai renyuan quan wei nanren de xueyuan zhong danren xiaozhang de nuxing Former personnel all be man DE college in hold-post head DE female (6) Drastic campaign reform is motivated by the desire to take campaigns out of unregulated hurly-burly of politics… ◰䘋Ⲵ ㄎ䘹 ᭩䶙 ᱟ ਇ 䘉ṧ а⿽ ᝯᵋ ੟ࣘ Ⲵ, Jijinde jinxuan gaige shi shou zheyang yizhong yuanwang qidong de, Radical campaign reform be PSV this one kind desire start DE ণ ㄎ䘹䘀ࣘ 㾱 ᩶㝡 н ਇ 㓖ᶏ Ⲵ ᭯⋫ ௗ䰩… Ji jingxuan yundong yao baituo bu shou yueshu d zhengzhi xuannao… i.e. campaign movement must get-rid-of not PSV restrict DE political noise (7) The two chief types of these programmes are Individual Retirement Accounts (IRAs) and Keogh plans. 䘉 ⿽ 䇑ࡂѫ㾱 ᴹ є ㊫, ণ њӪ 䘰Ձ䠁䍖ᡧ Zhe zhong jihua zhuyao you liang lei, ji geren tuixiujin zhanghu This kind plan chieﬂ y have two type i.e. individual pension account ઼ สྕ 䇑ࡂǄ He ji’ao jihua. And Keogh plan

In this chapter, we have discussed in great detail the lexical characteristics of translational Chinese in relation to non-translational or native Chinese. We have analysed and compared the keywords, key word classes, distribution of word class es, word-class cluster s or colligations as the interface of lexicon and syntax and distribution of punctuation and have undertaken four case studies which are closely related to translation universals, i.e. pronouns, connectives, idioms and reformulation markers, with the aim of describing the quantitative features of translational Chinese. In the following chapter, we will move from lexicon to syntax of translational Chinese relative to its native counterpart. Chapter 7 The Grammatical Features of Translational Chinese

Having provided meticulous descriptions of the lexical properties of translational Chinese, we will now look further into the grammatical features of translational Chinese by investigating and comparing a wide range of linguistic features in translated and native Chinese corpora. Many of these features are unique syntactical constructions and grammatical structures of Chinese such as the 被 bei passive construction (7.1 ), the disposal把 ba construction (7.2 ), the existential 有 you sentence (7.3 ), the copula是 shi (7.4 ), the所 suo construction (7.5 ) and the 连 lian construction ( 7.6) as well as grammatical categories such as classiﬁ ers ( 7.7), aspect markers (7.8 ), structural auxiliaries (7.9 ) and modal particles (7.10 ).

7.1 Bei Passive Constructions in LCMC and ZCTC

Passives are of interest because of their different functions in Chinese and English. Moreover, as noted in Chap. 4, the samples in our translational corpus ZCTC are mostly translated from English. In addition to a basic passive meaning, the primary function of passives in English is to mark an impersonal, objective and formal style, whereas passives in Chinese are typically a pragmatic voice carrying a negative semantic prosody (Xiao et al. 2006 : 143–144). As such, a comparison of the distribution patterns of passives in native and translated language will reveal whether translated Chinese demonstrates a tendency for normalisation or whether passives are used in contexts where native Chinese would typically not use passives because of source language shining through . This section compares the distribution patterns of 被 bei passive constructions in translational and native Chinese. Chinese employs a wide range of devices to express passive meaning, either in marked or unmarked form. While marked passives can be realised with syntactic or lexical passive markers, passives in Chinese can also take the unmarked form, in which no grammatical or lexical passive markers are used. Constructions of this

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 121 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_7 122 7 The Grammatical Features of Translational Chinese kind are often referred to as “notional passive sentences”. Subjects in these unmarked sentences are all patients. As in example (1), the subject of this sentence 文章 wenzhang “article” is inanimate, i.e. an article has been fi nished rather than has fi nished itself. The most important passive marker in Chinese is 被 bei, which can mark passive constructions with or without an agent. Alternatively, passives in Chinese can be marked by oral forms such as让 rang (2), 叫 jiao (3), 给 gei (4), and the archaic 为/被…所 wei/bei …suo structure (5). In addition to the fully fl edged bei and the partly grammaticalised rang , jiao and gei, there are a number of lexical verbs with an inherent passive meaning, for example, 挨 ai “suffer, endure” (6); 受 shou “suffer, be subjected to” (7); and遭 zao “suffer, meet with” (8). Furthermore, a number of Chinese syntactic constructions can express passive meaning as well, for example, …是…的 shi…de… (9) and加以 jiayi “add to” (10) (Xiao et al. 2006 ):

(1) 文章写完了。 Wenzhang xie wan le. Article write-fi nish-ASP. The article has been fi nished. (2) [她]正在洗的菜也让流水冲走了。 [Ta] zhengzai xi de cai ye rang liushui chongzou le [She] ASP wash GEN vegetable also PSV fl owing-water wash-away-ASP The vegetables [she] was washing were also washed away by the fl owing water. (3) 这下不叫我猜准了? Zhe-xia bu jiao wo cai-zhun-le? This-CL not PSV I guess-right-ASP Haven’t I guessed right this time? (4) 我妈妈也给辞了。 Wo mama ye ge i ci-le I mother also PSV fi re-ASP My mother was also fi red. (5) 她为他的爱所感动,她决定全力支持他的事业。 Ta wei ta de ai suo gandong, ta jueding quanli zhichi ta de shiye She PSV he GEN love PRT move, she decide full support he GEN career She was moved by his love and decided to support his career fully. (6) 孩子在家挨了打。 Haizi zai jia ai le da. Child at home suffer-ASP beat The child was beaten at home. (7) 纺织行业大受打击。 Fangzhi hangye da shou daji. Textile industry big PSV strike The textile industry was (8) 他无端遭了抢白,隐隐有些烦躁。 Ta wu duan zao le qiangbai, yinyin youxie fanzao. She no reason PSV ASP scold, hiddenly a little irritated She was cross in her heart because of being scolded for no reason. 7.1 Bei Passive Constructions in LCMC and ZCTC 123

(9) 历史是人民创造的。 Lishi shi renmin chuangzao de . History is the people create-GEN. The history is created by the people. (10) 有些问题要加以认真讨论。 Youxie wenti yao jiayi renzhen taolun. Some question should add to serious discussion Some questions should be discussed seriously. While passives in Chinese can be marked lexically or syntactically, or completely unmarked as reviewed above, we will only consider the “default” passive form marked by bei, which is also the most important and frequent type of passive construction in Mandarin Chinese. Bei can mark passive constructions with or without an agent. We will refer to passive constructions proﬁ ling the agent as “ long passives ”, as in (11) and those without proﬁ ling the agent as “ short passives ” (12): (11) 事实上, 他们却一个个被人杀了。 Shishishang, tamen que yi-gege bei ren sha-le In-fact, they but one-by-one PSV somebody kill-ASP But in fact, they were killed one by one (by somebody). (12) 敌人被打败了。 Diren bei dabai-le Enemy PSV defeat-ASP The enemy was defeated.

Figure 7.1 shows the proportions of short and long passives in native and translational Chinese, and for comparative purposes, the corresponding fi gures in the native English corpus FLOB are also included. As can be seen, although short passive s are more frequent than long passive s in both native and translational Chinese, the proportion of short passives in ZCTC is signifi cantly greater than in LCMC ( LL = 63.1 for 1 d.f., p < 0.001). The higher proportion of short passive s in translational Chinese is clearly a result of source language interference, because the short passive is the statistical norm of passive use in English (see Xiao et al. 2006 ), which accounts for over 90 % of the total, as shown in Fig. 7.1 . The passive in English is a strategy for expression in that it is used when the agent is unknown or there is no need to mention the agent (see Granger 1976, 1983). In Chinese, in contrast, three out of the fi ve syntactic passive markers (wei…suo, jiao, rang ) can only occur in long passive s, while the proportions of short passive s for the other two (60.6 % and 57.5 % for bei and gei , respectively) are considerably lower than that of English passives (cf. Xiao et al. 2006 ). As earlier Chinese grammarians Lü and Zhu ( 1979 ) and Wang (1954/1985) noted, the agent must be included in the Chinese passive, though this constraint has become more relaxed. When it is hard to identify the agent, vague expressions such as 人 ren “person, someone” or 人们 renmen “people” is specifi ed as the agent, which seldom occurs in English passive use. In cases where English uses the passive but does not profi le the agent, Chinese tends to avoid the passive. 124 7 The Grammatical Features of Translational Chinese

100 90 80 70 60 50 40 LCMC 30 ZCTC 20 10 FLOB 0 Short passive Long passive LCMC 60.7 39.3 ZCTC 74.3 25.7 FLOB 89.2 10.8

Fig. 7.1 Short and long forms of bei passives in three corpora

Passives in English and Chinese have different functions. English passives primarily function to mark a formal, objective and impersonal style and are thus prag- matically neutral, whereas Chinese passives are an “infl ictive voice” that tends to express a negative pragmatic meaning, evaluating the event being described as undesirable, unfavourable or adversative (Xiao et al. 2006 ). This is because the pro- totypical passive marker 被 bei is derived from a verb in ancient Chinese which meant “suffer”. Consequently, many disyllabic words with 被 bei in modern Chinese refer to something undesirable, e.g. 被捕 beibu “be arrested”, 被俘 beifu “be cap- tured”, 被告 beigao “the accused”, 被害 beihai “be victimised” and 被迫 beipo “be forced”, though the semantic constraint on passive use in modern Chinese is no longer as rigid as before (Xiao et al. 2006 ). Figure 7.2 compares the pragmatic meanings expressed by bei passive s in LCMC and ZCTC and by English be passives in FLOB . As can be seen, there are signifi cant differences in the proportions of different pragmatic meanings between the three corpora (LL = 212.28 for 2 d.f., p < 0.001), with the translational Chinese corpus positioned between the native Chinese and native English corpora and particularly marked contrasts in neutral and negative meaning categories. As English is the major source language of the translated texts in ZCTC, it is apparent that the translated Chinese texts are signifi cantly infl uenced by the English. Native and translational Chinese also differ in the overall frequency of passive use and in the distribution of passives across genres. Figure 7.3 shows the normalised frequencies of passives in the 15 genres as well as their mean frequencies in the ZCTC and LCMC corpora. As indicated by the mean frequencies, passives are more frequent in translational Chinese, and the log-likelihood (LL ) test indicates that the difference is statistically signifi cant (LL = 69.59 for 1 d.f., p < 0.001; see Table 7.7 ). Since passives are nearly ten times as frequent in English as in Chinese (Xiao et al. 2006 : 141–142), it is hardly surprising to fi nd that passives are signifi - cantly more common in Chinese texts. 7.1 Bei Passive Constructions in LCMC and ZCTC 125

90.0 80.0 70.0 60.0 50.0 40.0 LCMC 30.0 ZCTC 20.0 10.0 FLOB 0.0 Positive Neutral Negative LCMC 10.7 37.8 51.5 ZCTC 5.2 64.3 30.5 FLOB 4.7 80.3 15.0

Fig. 7.2 Pragmatic meanings expressed by bei passives

40 35 30 25

20 LCMC 15 ZCTC 10 5 0 J F P L E R B C N G H K A D M Mean

Fig. 7.3 Distribution of bei passives in LCMC and ZCTC

Figure 7.3 also shows that there is considerable variability across genres. Table 7.1 gives the result of the log-likelihood test for difference in each genre, with the signifi cant results highlighted. A combined reading of Fig. 7.3 and Table 7.1 reveals that in genres of expository writing such as press reportage (A), press reviews (C), instructional texts for skills, trade and hobbies (E), reports and offi cial documents (H) and academic prose (J), passives are signifi cantly more frequent in translational Chinese. The contrast is less marked in genres of imaginative writing (K–R). In imaginative writing, signifi cant difference is found only in the genre of mystery and detective fi ction (L), where passives are signifi cantly more common in native Chinese. 126 7 The Grammatical Features of Translational Chinese

Table 7.1 Log-likelihood Genre LL score Signiﬁ cance tests for passives in ZCTC A 8.65 p = 0.003 and LCMC B 1.83 p = 0.176 C 38.61 p < 0.001 D 1.93 p = 0.165 E 13.29 p < 0.001 F 3.17 p = 0.075 G 2.16 p = 0.142 H 155.68 p < 0.001 J 27.75 p < 0.001 K 0.88 p = 0.347 L 13.56 p < 0.001 M 0.45 p = 0.502 N 3.24 p = 0.072 P 0.06 p = 0.802 R 1.72 p = 0.189 Mean 69.59 p < 0.001

The different distribution patterns of passives in translational and native Chinese provide evidence that translated Chinese is distinct from native Chinese. Such patterns are closely related to the different functions of passives as noted earlier in Chinese and English, the overwhelmingly dominant source language in our translational corpus (cf. Sect. 4.3). Since mystery and detective fi ction is largely concerned with victims who suffer from various kinds of mishaps and the attentions of criminals, it is hardly surprising to fi nd that the infl ictive voice is more common in this genre in native Chinese. On the other hand, expository genres like reports and offi cial documents (H), press reviews (C) and academic prose (J), where the most marked contrast is found between translational and native Chinese, are all genres of formal writing that make greater use of passives in English. When texts of such genres are translated into Chinese, passives tend to be overused because of source language interference or shining through ; that is, native speakers of Chinese would not normally use the passive when they express similar ideas, for example, the following translated sentence taken from the translated Chinese corpus, ZCTC:

(13) 该证书就必须被颁发。 Gai zhengshu jiu bixu bei banfa This certiﬁ cate then must PSV issue Then this certiﬁ cate must be issued.

It is clearly a direct translation of the English passive, “Then the certifi cate must be issued”. In this case, a native speaker of Mandarin is very likely to use the so- called unmarked “notional passive”, i.e. the passive without a passive marker, which is very common in Chinese, as in the above example, “this certifi cate then must issue”. This is clearly a case of source language shining through . It is presently 7.1 Bei Passive Constructions in LCMC and ZCTC 127 not clear to what extent translated Chinese is affected by the source language in the translation process, which is part of our future investigation based on parallel corpus analysis in our project. However, available evidence of this kind does suggest that normalisation may not be a universal feature of translational language (cf. Sect. 3.1.3 ). The fact that bei passive constructions are more frequent in translated Chinese than in native Chinese is in line with Teich (2003 ) who fi nds that translated German also shows signifi cantly higher frequency of passive voice than native German (Teich 2003 : 196). The similar fi ndings of overuse of passives in translated texts in two distant languages provide interesting evidence for source language interference (Toury 1995 ) or shining through (Teich 2001 , 2003 ) but against normalisation in translated Chinese in terms of passive use. The above discussion has raised a question: since the evidence shows that passive structures in translated Chinese corpus is affected by interference of the English source language, to what extent, then, is it affected? We will look into this question by using the English-Chinese parallel Babel corpus. As shown in Fig. 7.4 , we search for the explicit bei passive marker in Babel and get the parallel concordance lines in both translated texts and correspondent source texts. According to these concordance lines in Fig. 7.4 , there are altogether 526 occurrences of bei passive constructions in Babel. After further classifi cation, we fi nd among the 526 passive constructions, there are 446 occurrences that are directly translated from English passive sentences (including those having semi-copulative verbs, e.g. feel , get , become , look , remain and seem ; see the following examples). The remaining 80 passive constructions are not from English passive sentences.

Fig. 7.4 Parallel concordance of bei passives in Babel 128 7 The Grammatical Features of Translational Chinese

This indicates that, in English-to-Chinese translation, a majority (85 %) of passive structures in translated Chinese are directly transferred from English as the source language, which is in line with Teich’s (2003 : 196) conclusion. Even those passives with no apparent source language passive sources are in fact caused by particular English expressions. For example: (14a) If he turns out to be a presentable, coherent but otherwise ordinary young man – reasonably law-abiding, amused by his good fortune and never taking himself too seriously – he will be from time to time spotted by photographers, snapped with girlfriends, mentioned in gossip columns, invited onto talk shows and we will know him so well we won’t care that, strictly speaking, somebody else was born ﬁ rst. (14b) 如果他长成了一个体面的、思路清晰的、而在其他方面又同于一般的年轻人-相当地遵纪守法,为他的好运而高兴,从来不把自已太当回事-他会时常被被摄影摄影师师捕捉捕捉 , 与女朋友在一起的时候被被拍快照拍快照 , 在闲聊栏目中被被提及提及 , 被被邀请参加电视访谈节目邀请参加电视访谈节目 , 而我们将如此地了解他,严格说来,我们不会在乎第一个出生的其实另有其人。

In example (14), the English passive structure, “be spotted by photographers, snapped with girlfriends, mentioned in gossip columns, invited onto talk shows”, has only one be verb, whereas the translated Chinese sentence has four explicit bei constructions. Source language interference can also be observed in the following examples (15–19), in spite of the fact that most of the bei passive markers should be omitted under normal circumstances from a native Chinese speaker’s view:

(15a) As American attitudes towards minorities and towards ethnic differences have changed in recent years, the long-reviled Chinese have gained wide acceptance. (15b) 因近年来美国人对少数民族和对种族、宗教上的文化差异的态度有所改变,长期被被谩骂的谩骂的中国人已得到广泛的承认。 (16a) The word “gift” has got dangerously devalued of late. (16b) “ 礼物”一词近来已被被危险地贬值危险地贬值了。 (17a) Studies show that, contrary to the belief of many physicians, an overwhelming majority of patients do want to be told the truth, even about grave illness, and feel betrayed when they learn that they have been misled. (17b) 恰恰与很多医师的观点相反,研究表明绝大多数患者确实希望被告知真实病情,哪怕病情严重。一旦得知受到欺骗,他们就有被出出卖的卖的感觉。 (18a) One theory is that fatty acids and the bile acids released to process them damage the cells and stimulate abnormal growth. (18b) 有一理论指出, 被被释放出来释放出来处理高脂食品的脂肪酸和胆汁酸破坏了这些细胞并促使它们生长异常。 (19a) Or at least I was an extra in a long-forgotten Crocodile Dundee sequel. (19b) 或者至少我是早被被遗忘的遗忘的《鳄鱼邓迪》续集中的替补人选。 7.1 Bei Passive Constructions in LCMC and ZCTC 129

We have shown previously in this section (see Fig. 7.3 ) that native and translational Chinese differ in the overall frequency of passive use and in the distribution of passives across registers and genres. In general, the relative frequencies of passives in translated Chinese texts of non-literary registers are higher than that in native Chinese, and the differences are of statistical signifi cance, particularly in genres such as press reportage (A), press reviews (C), skills/trades/hobbies (E), miscellaneous (H) and academic prose (J). In contrast, in literary registers and genres, passives in translations and native writings are very close to each other, with a few genres (e.g. detective fi ction (L)) having signifi cantly higher frequency of passive use in native texts than in translated texts. As such, the distribution of passives in translation and non-translation is closely related to the differences between literary and non-literary text categories. Unfortunately, the Babel corpus does not distinguish text categories, registers and genres of texts and, therefore, is unfi t for exploring the relationship between source language interference and register variation. As a consequence, we use another parallel corpus, the General Chinese-English Parallel Corpus ( GCEPC ) hosted in Beijing Foreign Studies University (see Sect. 4.4.2 ), to continue the discussion. Figure 7.5 shows the relative size of SL interference and non-SL interference in terms of bei passive constructions in the two general text categories, literature and non-literature. As can be seen, there are totally 553 occurrences of bei passives in the literary text category of GCEPC , among which 405 are directly transferred from English passives, taking about 73 % of all passives. Contrastively, there are 768 occurrences of bei passives in the non-literary texts; among them, 712 passives are translated from English, taking 93 % of all passives in the non-literary category. These comparisons indicate that SL interference from English in the non-literary category is stronger than that in the literary category, which is presumably because in English non-literary source texts (e.g. offi cial documents and science), there exists a tendency of excessive use of passives (Baker 1985):

Fig. 7.5 SL interference 900 presented by bei passives 800 56 from English to Chinese 700 600

500 148 400 712 300 200 405 100 0 LITERATURE NONLITERATURE

SL interference Non-SL-interference 130 7 The Grammatical Features of Translational Chinese

(20a) There were many easy ways of doing this. The pleasantest was to dine luxuriously at some expensive restaurant; and then, after declaring insolvency, be handed over quietly and without uproar to a policeman. (20b) 要兑现自己的意愿,有许多简捷的途径,其中最舒服的莫过于某家豪华餐厅大吃一顿,然后呢,承认自己身无分文,无力支付,这样便安安静静、毫不声张地被交给警察。

In example (20), the English passive structure “be handed over” is translated into bei jiaogei le jingcha “PSV give ASP police”, which is indeed very weird from the native Chinese perspective (because the passive marker bei is totally unnecessary) and therefore is an obvious transfer from the English source language. Similarly, in the following examples (21–23), the passives may also be retraceable to source language interference for the reason that all bei passive markers in them should be removed:

(21a) But we do have convincing information obtained from magnetised mineral grains in rocks that tells us that geomagnetic polarity reversals have occurred a great many times in the history of the earth. (21b) 但是我们确实从岩石被被磁化的磁化的矿物颗粒中获得了令人信服的信息,这个信息告诉我们在地球史上曾发生了很多次地磁极性倒转。 (22a) In practice, it is not always easy to establish a person as a shadow director of a company, which involves the proof that the relevant board of directors has been failing to exercise any discretion or judgement, contending instead to act upon the directions and instructions of the alleged shadow director as a matter of practice over a period of time. (22b) 就实际情况来说,要确定一个人是不是影子董事并不容易,这牵涉到是否能够证明:有关的董事会长久以来推卸董事应尽的责任,而仅遵照被断断言言为影子董事的人的指导、指示办事。 (23a) I was scared shitless , I can tell you, mister, because it was clear as mud to me from the start that those lousy golden gadgets could bring us nothing but a load of misery. (23b) 我被被吓吓得得屎都拉不出屎都拉不出 , 伙计! 因为一开始我就一清二楚:那些金色的玩意,除了一大堆灾难外,什么也不会带给我们。

As these examples demonstrate, our previous argument that bei passive constructions are overused in translated Chinese mostly because of source language interference from English has been supported by results from both comparable and parallel corpora. Source language interference in passive use exists in either literary or non- literary texts, with the latter being more signifi cantly affected, while this difference is related to the distribution of passives in various registers and genres in English (cf. Xiao et al. 2006 ). The relative stronger performance of passive structures in the non- literary category or indifference of them in the literary category seems to mirror the distribution of English passives in literary and non-literary texts. More specifi cally, the overrepresentation of passives in non-literary genres such as offi cial documents 7.2 Ba Disposal Constructions in LCMC and ZCTC 131

(H), press review (C) and science (J) may have come from the excessive use of passives in these genres. When translating texts in these genres, most of the English passives are directly translated into Chinese bei passive constructions and hence caused the overuse of bei in translated texts. This is, in our view, explicable to source language interference ; meanwhile, the extent to which translational Chinese is affected largely depends on the variation of these structures in the source language.

7.2 Ba Disposal Constructions in LCMC and ZCTC

The disposal 把 ba is one of the most important and commonly used constructions in Chinese. It is a unique sentence structure in Chinese, which can hardly fi nd any equivalent in European languages such as English. Only verbs with a proposal meaning can occur in the ba construction, where ba is a preposition that moves the grammatical object of a verb from its normal position following the verb to a pre- verbal position to highlight the object as well as the action denoted by the verb and its result. The disposal construction also has the cohesive discourse function as moving the verbal object away from its usual position frees the verb up so that it can combine with other sentential elements more closely to express complex ideas. There are numerous studies on the ba construction in the literature: early represen- tatives include Wang ( 1943 , 1944 ), Lü (1984), Ding ( 1980 ) and Zhu ( 1980 ); more recent discussions are Shao ( 1985 , 2002 ), Fan ( 1998 , 2001 ), Zhang ( 1991 ), Jin ( 1997 ) and Zhang ( 2000 ), among many others. These studies cover a wide range of topics with regard to syntactic, semantic and pragmatic characteristics of the ba construction and their pedagogical applications to teaching Chinese as a foreign language (cf. Ke 2003 ). Shao ( 2002 : 76) simplifi es the basic structure of the ba construction into a formula as follows, “the ba construction = Subject + ba + Object + (Modifi er 1) + Verb + (Modifi er 2)”, where () stands for possible positions. The two modifi ers can appear in the meantime, but at least one must be used in the construction. Modifi er 2 after the verb can be the complement, the object and aspect markers such as le , zhe , guo , 起来 qilai “going on”, 下去 xiaqu “going down”, etc., while Modifi er 1 is adverbial. As for the verb in the ba construction , some grammarians point out that the verb in a ba construction is mainly disposal or causative. This is because the object has been highlighted by preceding the verb which is expected to cause something to happen to the object. Therefore, under normal circumstances, modal verbs, psych verbs or existential verbs like有 you “have” and没有 meiyou “not have” cannot be used as the predicate verb in the ba construction (Ke 2003 ). Ke (2003 ) notes that the ba construction has three basic functional features: fi rst, it moves the object to a preverb position; second, it highlights both the object and the action with its consequence; third, it helps to improve discourse cohesion by freeing the verb from being constrained by the verb-object relation, which brings more options for the verb to be followed by other grammatical components. The main semantic 132 7 The Grammatical Features of Translational Chinese function of the ba construction is to emphasise the disposal or causative action (i.e. making changes) and its target. For the sake of coherence, the ba construction also helps to balance the pre- and post-verb structures while highlighting the object. Due to the peculiarity of the ba construction, it is often an optimal or even the only grammatical choice under some particular circumstances, or else it would be very diffi cult to express certain meaning. As noted by Jin ( 1997 ), sometimes the ba construction is “irresistible to human willingness”, in a sentence such as the following:

(1a) *我种花在阳台上. *I plant fl ower at porch on (1b) 我把花种在阳台上 I ba fl ower plant at porch on (1c) I planted some fl owers in the porch.

Example (1a) is grammatically correct if we consider only the formal structure of it, whereas it is obviously against the pragmatic instinct of a native Chinese speaker who will agree that (1b) sounds more natural. The reason why this is so is probably because the transitive verb is freed from the immediate use of the object after it in the ba construction and hence is possible to be connected with the following prepositional phrase. As such, many Chinese grammarians think the ba construction may be a grammatical device to help Chinese to express more delicate meaning (cf. Shao 2002 : 76–96; Ke 2003 ). Based on this, some researchers propose when translating complex sentences from a foreign language, the translator will often have to seek for more delicate and probably more complicated expressions. Consequently, this ends up with the overuse of the ba construction in translated Chinese (Ke 2003 ) because it is meant for expressing complex and delicate meaning by nature. Ke ( 2003 ) tests this hypothesis by searching the General Chinese-English Parallel Corpus ( GCEPC ). He ﬁ nds that ba constructions are more common in literary texts than non-literary text, while no matter it is in literary or non-literary categories, translated Chinese texts have more frequent use of ba constructions (in the literary category, native texts have 2.02 occurrences of ba constructions per 1,000 words in contrast to 3.52 occurrences in translated texts; in the non-literary category, native texts have 1.01 occurrences per 1,000 words versus 2.07 occurrences in translated texts). Ke ( 2003 ) concludes, due to the peculiarity of the ba construction, it is more capable of encoding complex and delicate meaning; as a result, ba constructions are overused in translation, particularly in translation of complex sentences and structures. Hu and Zhu (2008 ) investigate the ba construction in two different Chinese translations (respectively, by Liang Shiqiu1 and Zhu Shenghao2 ) of Shakespeare’s Hamlet . They ﬁ nd in both translations, ba constructions are more frequently used

1 Liang Shiqiu (梁实秋), also Romanised as Liang Shih-chiu (1903–1987), was a renowned Chinese educator, writer, translator, literary theorist and lexicographer. 2 Zhu Shenghao (朱生豪) (1912–1944) was a renowned Chinese translator. He was among the ﬁ rst few in China who translated the works of William Shakespeare into Chinese language. His translations are well respected by domestic and overseas scholars. 7.2 Ba Disposal Constructions in LCMC and ZCTC 133

300 250 200 150 100 50 0 General Academic News Fiction Mean Normalised frequencyNormalised per 100,00 words prose prose LCMC 177 184 146 224 187 ZCTC 123 175 109 269 181

Fig. 7.6 ba constructions in LCMC and ZCTC than comparable native literary texts; however, the two versions are also different with each other: Zhu’s translation has higher frequency of ba constructions than comparable Chinese dramas, 《雷雨》 Leiyü “Thunderstorm”3 (by 47 %) and 《茶馆》 Chaguan “Teahouse”4 (by 69 %); the frequency is strikingly higher than that of another contemporary Chinese novelist’s collections, Chi Li’s Recent Novels, 5 by 4.5 times. In contrast, Liang’s translation has more ba constructions than Leiyü and Chaguan , respectively, by 10 and 47 %, but less than the other two native Chinese novels, 《骆驼祥子》 Luotuo Xiangzi “Rickshaw Boy”6 and 《林家铺子》 Linjia Puzi “The Shop of the Lin Family” 7 (Hu and Zhu 2008 : 112). These comparisons seem to indicate that there are individual variations of ba constructions among different writers and translators even in the same or similar registers. In the remainder of this section, we will compare the use of the ba construction in native and translational Chinese. Figure 7.6 and Table 7.2 show the distribution of ba constructions in terms of normalised frequency (per 100,000 words). It can be seen that the overall mean frequencies in the two corpora are close to each other, a difference without statistical signifi cance ( LL = 0.87 for 1 d.f., p = 0.351), but there are register variations between native and translated texts. Ba constructions are most frequent in fi ction and least frequent in academic prose in both native and translational Chinese possibly because ba sentences are descriptive in nature (cf. H u 2008a ). In fi ction, ba constructions are more common in translational Chinese,

3 Thunderstorm (雷雨) is a play by the Chinese dramatist Cao Yu. It is one of the most popular dramatic Chinese works of the period prior to the Japanese invasion of China in 1937. 4 Teahouse (茶馆) is a three-act play published in 1957 by the Chinese dramatist Lao She (老舍, 1899–1966). 5 Chi Li (迟莉) (1957–) is a contemporary Chinese female novelist. 6 Rickshaw Boy (骆驼祥子), also known as “Camel Xiangzi” or “Rickshaw”, was published in 1936. It is the most important novel written by Lao She (1899–1966), a renowned Chinese dramatist. 7 The Shop Of the Lin Family (林家铺子), published in 1932, is a novel written by Mao Dun (矛盾) (1896–1981), a twentieth-century Chinese novelist, cultural critic and journalist. 134 7 The Grammatical Features of Translational Chinese

Table 7.2 Log- likelihood tests for 把 ba constructions in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance News (A–C) 332 265 9.28 0.002 General prose (D–H) 810 781 1.07 0.301 Academic prose (J) 249 193 9.09 0.003 Fiction (K–R) 612 743 11.17 0.001 Mean (A–R) 2,003 1,982 0.870 0.351 whereas in the other three registers, they are more frequent in native Chinese, though the contrast in general prose (the difference is not signiﬁ cant) is not as marked as in news and academic prose. As the ba construction usually has the meaning of disposal, it is generally agreed among Chinese grammarians that the verb in this construction cannot be used without following grammatical components with exceptions of some special genres such as poetics. For example, Lü (1942 : 58) notes that one necessary structural condition for the ba construction is “something has to follow the verb”. Chao and Yang (1947: 89) points out that “the ba sentence is used only when the main verb is followed by a complement or a numeral plus an auxiliary noun compatible with the verb, or a repeated verb”. Wang ( 1954 : 163) gives a similar comment, “a disposal structure should not be followed only by a single verb, but should be used together with a supplementary complement”. As such, the supplementary component in the ba construction is usually resultative in nature, which has the same effect of resultative verb complements (RVCs). The situation that the ba construction describes normally suggests the result which is uncancellable (Xiao and McEnery 2004 ). Let us compare the following sentences:

(2a) 我去邮局寄包裹了, 可是没寄走, 因为邮局关门了 I go post offi ce mail parcel ASP, but not post-go, because post offi ce close-door ASP I went to the post offi ce to mail a parcel, but didn’t mail it out, because the post offi ce was closed. (2b) 我去邮局把包裹寄了,*可是没寄走,因为邮局关门了。 I go post offi ce ba parcel mail ASP, *but not post-go, because post offi ce close-door ASP I went to the post offi ce and mailed a parcel , *but didn’t mail it out , because the post offi ce was closed.

Example (2a) has no ba construction in it, so the result is cancellable; whereas example (2b) has a ba construction which suggests the result is already made and uncancellable, therefore the next part of the sentence is illogical. This semantic peculiarity of the ba construction is in line with the general semantic confi guration of the narrative genres like fi ction, which is possibly one reason why the ba construction is more common in narration than in offi cial documents and academic prose. This distribution pattern seems to suggest that the ba construction is also colloquial in nature (cf. Du 2005 ). This hypothetical comment 7.3 Existential You Sentences in LCMC and ZCTC 135 is supported by a research based on a one-million-word balanced spoken Chinese corpus, the Lancaster Los Angeles Spoken Chinese Corpus (LLSCC) (see Xiao and Tao 2007 ). As shown by this corpus, completely spoken genres (such as face-to-face conversations, television interviews and TV or fi lm scripts) have signifi cantly more occurrences of ba constructions than more formal spoken genres (such as public debates). This seems to indicate that in an optional situation of ba constructions, the choice of this structure may help to improve the readability of the text by making it more spoken styled or easier. Wang ( 2010 ) discusses the three meta-functions (i.e. ideational, interpersonal and textual functions in systemic functional linguistics) of the ba construction and their realisation in English-Chinese translation. Here, research shows at the textual level the subject and object of the ba construction function, respectively, as the major and secondary topics, which are connective in the topic chain within the text. This textual connective function is similar to the coherence and cohesion function of pronouns and conjunctions discussed previously in Chap. 6 . The discussions above seem to suggest that the frequency of ba construction s in translated Chinese should be higher than native Chinese. This is supported not only by our analysis of the register of fi ction but also by Ke (2003 ) and Hu and Zhu (2008 ). However, in the other three non-literary registers, our data indicates that, in news and academic prose, ba constructions are more common in native Chinese than in translated Chinese, while the difference between native and translated texts has no statistical signifi cance in general prose. As the ba construction is a colloquial feature and has the textual function of cohesion at the discourse level, it can be used to make text easier to read (cf. Xiao 2012). The more frequent use of ba constructions in translated fi ction, which is for light reading, can be considered as an indicator of simplifi cation . When a register is not for light reading, e.g. in news and academic writing, ba constructions are under-represented in translation. The conclusion is different from Ke (2003 ) in terms of the distribution of ba constructions in the non-literary text category. But the corpus used in his study mainly consists of political documents, such as collective works by Mao Zedong and Deng Xiaoping, legislative clauses and regulations and government white papers, which are comparable to offi cial documents in general prose of our corpus. If we take into account other non-literary texts in a more balanced way, the conclusion should be different from Ke’s ( 2003 ).

7.3 Existential You Sentences in LCMC and ZCTC

The existential有 you “have” is another high-frequency basic Chinese syntactic construction which has a variety of forms and meanings. In his The Gist of Chinese Grammar ( 1942 ), Lü Shuxiang8 ﬁ rst proposed to study the existential you sentence as a special syntactic structure in Chinese. Numerous studies of this structure have been conducted ever since Lü’s 1942 research, among which there are two types of

8 Lü Shuxiang (吕叔湘, 1904–1998) was a distinguished linguist, lexicographer and educator and founder of Modern Chinese linguistic studies. 136 7 The Grammatical Features of Translational Chinese defi nitions: in the narrow sense, the existential you sentence refers to the sentence in which you (and its negation forms, 没有 meiyou “not have”, 没 mei “not (have)”) is used as the predicate verb, while, in the broad sense, it includes any sentences which contain the existential marker you and its negation forms regardless of whether you is the predicate or not. Most Chinese grammarians agree with the defi nition in the narrow sense (e.g. Lü 1942 ; Li and Liu 1957 ; Fan 1987 ; Yi 1994 ), that is, only when you is the predicate verb of a sentence is it considered as an existential you sentence . We will also take the narrow defi nition in our discussion, for the reason that some of the non-predicate you structures have already been established as near pronoun s due to high-frequency co-occurrences of you and other grammatical components, for example, 有的 you de “have some, some” and 有人 you ren “have people, someone”. These phrases used to be verb-object structures, but now are usually used as pronouns in modern Chinese, i.e. the sequence you de is used as one word meaning “some”; and you ren is a word that means “someone, somebody”. The existential marker有 you is a special non-actional verb which has basic meanings of “to possess” as in example (1) and “to exist” in example (2). But it has a series of derivative meanings based on the two basic meanings, for instance, “to appear or emerge” in example (3), “to juxtapose” in example (4), “to include” in example (5) and “to reach” in example (6) (cf. Zheng 2010 ). It is clear that the existential you is a commonly used construction that is abundant in different forms and meanings:

(1) 我有一本书. Wo you yiben shu I have a-CL book I have a book. (2) 桌子上有一本书. Zhuozi shang you yiben shu Table on have one-CL book There is a book on the table. (3) 孩子的学习有了明显的进步. Haizi-de xuexi you -le mingxian-de jinbu Child-GEN study have ASP signifi cant-GEN progress There appear signifi cant progresses in the child’s study. (4) 操场上的人, 有跑的, 有跳的, 有打篮球的. Caochang shang de ren, you pao-de, you tiao-de, you da-lanqiu-de Playground on GEN people, have run-GEN, have jump-GEN, have play-basketball-GEN The people on the playground are running, jumping and playing basketball. (5) 一天有二十四小时 Yi tian you ershi-si xiaoshi One day have 24 h A day has 24 h. (6) 那条鱼有斤把重 Na tiao yu you jin-ba zhong That CL fi sh have CL-about weight That fi sh’s weight reaches about a pound. 7.3 Existential You Sentences in LCMC and ZCTC 137

800 700 600 500 400 LCMC 300 ZCTC 200 100 0 A-C D-H J K-R Mean LCMC 548 640 559 745 638 ZCTC 581 604 490 645 592

Fig. 7.7 Normalised frequency of existential you in LCMC and ZCTC

Table 7.3 Log-likelihood tests for existential you in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance News (A–C) 1,024 1,113 1.81 0.178 General prose (D–H) 2,822 2,073 126.83 0.000 Academic prose (J) 956 866 5.47 0.019 Fiction (K–R) 2,036 1,782 19.88 0.000 Mean (A–R) 6,838 6,464 18.73 0.000

As shown in the analyses of key word class es in Chap. 6 (see Sect. 6.1 ), the verb you (part-of-speech tag, VYOU) is one of the negative key word class es in the translational Chinese corpus, ZCTC. This means the existential verb you is signifi - cantly less frequently used in translational Chinese than in native Chinese. Figure 7.7 shows the distribution of you in LCMC and ZCTC (in normalised frequencies per 100,000 words). The frequencies in this fi gure include both the positive and negation forms of you, i.e. you “have”, meiyou “not have” and 没 mei “not (have)”. As can be seen, in general, native Chinese corpus LCMC has higher frequency of you constructions than translational corpus ZCTC. As the LL tests in Table 7.3 show, the difference is statistically signifi cant (LL = 18.73, 1 d.f., p < 0.001). There are also register variations of the existential you in terms of the difference between native and translational Chinese. As shown in Fig. 7.7 , on the one hand, native and translational Chinese have similar distribution patterns across the four major registers: fi ction has the most frequent occurrences of you , followed by general prose, news and academic prose (in LCMC the difference between news and academic prose has no statistical signifi cance, i.e. LL = 0.21, 1 d.f., p = 0.649). As the basic and derivative meanings of you have shown, the existential you sentence has a descriptive function (cf . Zhang and Zheng 2006 ). Therefore, it is not surprising that fi ction and general prose have higher frequencies of you than academic 138 7 The Grammatical Features of Translational Chinese prose and news. On the other hand, you constructions are signifi cantly more common in native registers such as fi ction, academic prose and general prose than in comparable translational registers; although the frequency of you in news in the native Chinese corpus is slightly higher than in the same register in translational Chinese, the difference is not statistically signifi cant (LL = 1.71, 1 d.f., p = 0.178). As such, we can conclude that, in contrast to native texts, Chinese translations make less use of the existential verb you . As discussed at the beginning of this section, the existential you sentence is a basic Chinese syntactic structure abundant in forms and meaning. Because of this, the existential you is often regarded as a diffi cult structure in teaching Chinese as a foreign language (cf. Yu 2009 ). In this sense, the under- representation of you in translated Chinese seems to indicate a simplifi cation tendency in translation. As a unique structure in Chinese, the underuse of you can also be understood as a tendency of under-representation of target-language unique items.

7.4 Shi Sentences in LCMC and ZCTC

The copula 是 shi is the second most frequently used monosyllable verb in Chinese, only less common than the fi rst most frequent word, structural auxiliary的 de (Xiao et al. 2009 ). The so-called shi sentence by Chinese grammarians is a classic predicate sentence with the copula shi as the core predicate verb. The negative form of shi is不是 bushi “not be”. As noted by Liu ( 1991 : 141–145), the semantic functions of shi include “to equate” as in example (1), “to categorise” in example (2), “to describe” in example (3), “to show the existence of” in example (4) and “to describe the impression or consequence of” in examples (5) and (6). He also points out that shi has a strong static property, mainly used to express judgement, confi rmation or description (Liu 1991 : 137). In contrast to English, the copula be tends to be more dynamic; along with the function of expressing judgement or confi rmation, it can be used to describe a process, a change and sensuous activities of the subject (ibid: 146):

(1) 中国的首都是北京 Zhongguo de shoudu shi beijing China GEN capital city BE Beijing China’s capital city is Beijing. (2) 我是中国人 Wo shi zhongguo ren I BE China people I am a Chinese (3) 她是一个心直口快的人 Ta shi yige xinzhikoukuai de ren She BE one-CL heart-straight-mouth-fast GEN people She is an honest woman who speaks what she thinks. 7.4 Shi Sentences in LCMC and ZCTC 139

(4) 满屋子都是烟 Man wuzi dou shi yan Whole room all BE smoke The room is full of smoke. (5) 男孩子穿白衣,女孩是一色桔黄 Nanhaizi chuan baiyi, nǚhai shi yi se juhuang Male-child wear white clothes, female-child BE one-clour orange Boys wear white clothes, while girls are all in orange. (6) 你收款吧,他是四十二元,我是四十二元五 Ni shou kuan ba, ta shi si-shi-er-yuan, wo shi si-shi-er-yuan-wu You take bill PRT, he BE 42-CL, I BE 42-CL ﬁ ve You can take the bill now; his (bill) is 42 yuan, and mine is 42.5 yuan.

Xu ( 2007b ) classifi es shi sentence s based on their different styles and functions: the fi rst type is often found in imagery metaphors as in example (7), which is usually impressionistic and literary; the other type is common in rational judgement as in example (8), which usually appears in non-literary texts, e.g. scientifi c articles, practical writings, political arguments, etc. This section compares the performances of shi sentences in native and translational Chinese corpora:

(7) 情是圆的,理是方的。 Qing shi yuan de, li shi fang de Feeling BE round GEN, reason BE square GEN The emotion is like a circle; while the reason is like a square. (8) 实践是检验真理的唯一标准 Shijian shi jianyan zhenli de weiyi biaozhun Practice BE test truth GEN only-one standard Practice is the only one standard to test a truth.

Figure 7.8 shows the normalised frequencies per 100,000 words of shi sentence s in the native corpus LCMC and the translational corpus ZCTC . As can be seen, shi sentence s in general are more common in translated Chinese than in native Chinese, and as the LL test results in Table 7.4 reveal, the difference is of statistical signifi - cance (LL = 16.96, p < 0.001). In terms of variation in two broad categories, translated Chinese shows higher frequency in both literary and non-literary categories, although the difference in literary texts is not statistically signifi cant (p > 0.05). Nevertheless, shi sentences in the literary category are apparently more frequent than those in the non-literary category in both corpora. The relative over-representation of shi sentence s in the translated Chinese corpus ZCTC than the native corpus LCMC may be explicable to the infl uence of the commonly used English copula be , i.e. the interference of source language . Similar to the Chinese shi , the English copula be is also a super high-frequency verb. Furthermore, as previously discussed, be has a wider range of use than shi . This is one of the possible reasons why many English native speakers, when learning 140 7 The Grammatical Features of Translational Chinese

1,400

1,350

1,300

1,250 LCMC 1,200 ZCTC

1,150

1,100 Non-literary (A-J) Literary (K-R) Mean (A-R) LCMC 1,193 1,301 1,220 ZCTC 1,265 1,356 1,288

Fig. 7.8 Distribution of shi sentences in LCMC and ZCTC

Table 7.4 Log-likelihood tests for shi sentences in LCMC and ZCTC Register LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 9,524 10,321 19.54 0.000 Literature (K–R) 3,553 3,747 3.09 0.079 Mean (A–R) 10,377 14,068 16.96 0.000

Chinese as a foreign language, tend to overuse inappropriately shi constructions as exempliﬁ ed by the following ill sentences: *我今天是忙. (*Wo jintian shi mang .) *I today BE busy. “I am busy today”. *我是饿了. *Wo shi e le. * I BE hungry ASP. “I am hungry”. In both sentences shi seems to be directly transferred from the English copula. However, the overuse of English copula does not only exist in learners’ Chinese interlanguage, but it is also present in Chinese translations from English, as illustrated by the following sentences (9–12) from ZCTC:

(9) Probably, she will be passionate in bed. (9a) 也许,她在床上会是热情的。 Yexu, ta zai chuangshang hui shi reqing de Maybe, she at ben-on will BE passionate-GEN (9b) 也许,她在床上会很热情。 Yexu, ta zai chuangshang hui hen reqing Maybe, she at ben-on will very passionate (10) Despite of this, their differences are obvious. (10a) 尽管如此,它们的不同又是明显的。 Jinguan ruci, tamen de butong you shi mingxian de Although as this, they-GEN differences again BE obvious-GEN (10b) 尽管如此,它们的不同又十分明显。 Jinguan ruci, tamen de butong you shifen mingxian Although as this, they-GEN differences again very obvious-GEN 7.4 Shi Sentences in LCMC and ZCTC 141

(11) …it is never easy. (11a) …从来都不不是是容易的。 …conglai dou bushi rongyi de …ever since all not-BE easy-GEN (11b) …从来都不容易。 …conglai dou bu rongyi …ever since all not easy (12) Its effect is also not bad. (12a) 其效果也是不错的。 Qi xiaoguo ye shi bucuo de Its effect also BE non-bad-GEN (12b) 其效果也 (很) 不错。 Qi xiaoguo ye bucuo Its effect also not-bad

The examples in (9a) to (12a) above are all found in the translated Chinese corpus. Although they are not grammatically wrong if judged only by Chinese grammar, they do not read as idiomatic and natural as examples (9b) to (12b) in which the copular shi and the structural auxiliary de are dropped off. It seems that the translated sentences (9a) to (12a) have a strong tendency of direct transfer of the English copula be in the source texts (9) to (12) and hence end up with “translationese”. To further support this observation, we search for occurrences of the sequence shi + (adverb) + adjective + de + punctuation mark, that is, the particular shi pattern as presented in the above sentences, in the two corpora. It turns out that the native and translated corpora, respectively, have 456 and 578 occurrences of the pattern (43 and 53 occurrences in normalised frequencies per 100,000 words); the difference is also statistically signiﬁ cant ( LL = 12.19,1 d.f., p < 0.001). This indicates that the above translationese shi pattern is indeed signiﬁ cantly over-represented in translated Chinese. To what extent, then, is this pattern transferred directly from English as the main source language? We search the parallel Babel corpus for the pattern of “ be + adjective ”. Among the 568 occurrences of “be + adjective ”, 197 of them (34.7 %) in the source texts are directly translated into shi patterns; and these shi patterns are not as idiomatic as the sentences without shi. The following examples (13–16) are chosen from the Babel corpus which allow us to observe the transfer of be into Chinese translations. We can compare them with more natural native Chinese expressions in which the shi and de pattern (transcribed as BE…GEN) seems redundant:

(13) It was easy. (13a) 这是容易的。 Zhe shi rongyi de This BE easy-GEN (13b) 这很容易。 Zhe hen rongyi This (is) very easy 142 7 The Grammatical Features of Translational Chinese

(14) But all of them are vulnerable and all of them are tense. (14a) 但他们都是脆弱的,而且都是紧张的。 Dan tamen dou shi cuiruo de, erqie dou shi jinzhang de But they all BE fragile-GEN , also all BE nervous- GEN (14b) 但他们都很脆弱,而且都很紧张。 Dan tamen dou hen cuiruo, erqie dou hen jinzhang But they all very fragile, also all very nervous. (15) Human cloning is unsafe。 (15a) 人的克隆是不安全的。 Ren de kelong shi bu anquan de Human GEN clone BE not safe-GEN (15b) 克隆人不安全。 Kelong ren bu anquan Clone human not safe (16) Ben Franklin’s solution was elegant. (16a) 本 · 弗兰克林的方法是非常了不起的。 Ben · fulankelin de fangfa shi feichang liaobuqi de Ben Franklin-GEN method BE very wonderful-GEN (16b) 本 · 弗兰克林的方法非常了不起。 Ben · fulankelin de fangfa shi feichang liaobuqi de Ben Franklin-GEN method very wonderful

7.5 Suo Sentences LCMC and ZCTC

As shown in analyses of key word class es in Chap. 6, the structural auxiliary所 suo (part-of-speech tag USUO) is among the key word classes in the corpus of translated Chinese texts. The principal function of the structural auxiliary suo is to be used before a verb to form the so-called “ suo phrase”, i.e. to nominalise the verb (e.g. 所见所闻 suo jian suo wen [AUX see AUX hear] “what is seen and heard”; 所到之处 suo dao zhi chu [AUX reach GEN place] “the place reached”). However, except for such idiomised suo phrases as the above examples, other non-idiomised suo phrases cannot normally be used independently as syntactic components unless they are followed by another structural auxiliary的 de (e.g. 总体是人们所要研究的对象. zongti shi renmen suo yao yanjiu de duixiang. [Totality BE people AUX want study GEN object.] “Totality is the object that people what to study”.) The sentences that contain such usage of suo are called suo sentence s which are mainly used in formal and literate styles. Another common usage of suo is an archaic passive construction为/被…所… wei/ bei …. suo… (e.g. 为生活所迫、这已被大量事实所证明. Wei shenghuo suo po; zhe yi bei daliang shishi suo zhengming. [PSV life AUX force, this already PSV a lot (of) fact AUX prove.] “(They were) forced by life; this has already been proved with facts”.) We will discuss in this section the distribution of suo sentence s in native and translated Chinese. 7.5 Suo Sentences LCMC and ZCTC 143

250

200

150

100 LCMC ZCTC 50

0 A-C D-H J K-R Mean LCMC 64 107 234 42 103 ZCTC 144 219 196 77 166

Fig. 7.9 Distribution of suo sentences in LCMC and ZCTC

Table 7.5 Log-likelihood tests for suo sentences in LCMC and ZCTC Register LCMC ZCTC LL Signiﬁ cance News (A–C) 119 276 60.25 0.000 General prose (D–H) 472 978 172.34 0.000 Academic prose (J) 400 346 5.91 0.015 Fiction (K–R) 116 214 28.39 0.000 Mean (A–R) 1,107 1,814 159.43 0.000

Figure 7.9 shows the normalised frequencies per 100,000 words in both corpora. As can be seen, in general, the translational Chinese corpus ZCTC has a strikingly higher frequency of suo sentences than the native corpus LCMC ; and the difference is statistically signifi cant ( LL = 159.43, p < 0.001 see Table 7.5 ). There are also register variations in terms of the differences between translated and native corpora: in translated Chinese corpus, suo sentences are most common in general prose (D–H), followed by academic prose (J) and fi ction (K–R); in contrast, fi ction makes the least frequent use of suo sentences, whereas academic prose has a much more frequent use than the other two registers, news (A–C) and general prose (D–H). Therefore, in terms of register variation, suo sentences are more common in native academic prose than the translated counterpart; but in the other three registers, suo sentence s have higher frequencies in translated texts than in native texts. The results of log-likelihood tests in Table 7.5 show that these differences are of statistical signifi cance. The over-representation of suo sentence s in academic prose than the other registers in the native Chinese corpus may be explicable to the use of the archaic suo passive construction introduced above. This suo passive construction, i.e. 为/被… 所…wei/ bei …. suo… , is usually used in formal and literate styles including academic writings. For example: 144 7 The Grammatical Features of Translational Chinese

(1) 科学技术在社会经济发展中的重要性已经越来越被人们所认识. Kexue jishu zai shehui jingji fazhan zhong de zhongyaoxing yijing yuelaiyue bei renmen suo renshi. Science technology in society economy development middle GEN important-quality already PSV people AUX realise. The importance of science and technology in societal and economic development has already been realised by people.

In contrast, in other three registers other than academic prose, translated Chinese has signifi cantly higher frequency of suo sentence s than native Chinese. This may be related to some of the linguistic features of the SL: English has a large number of grammatical devices used in relation to nouns, such as relative clauses, non-defi nite attributive clauses, propositional phrases and WH clauses. These devices may be refl ected to some extent by the suo sentences in the TL, Chinese. For example, in the translated Chinese subcorpus of the parallel Babel corpus, there are 312 occurrences of suo sentences, among which 72 suo sentences are translated from English relative clauses, 36 occurrences are translated from non-defi nite attributive clauses (e.g. past participial relatives) and 27 are from post-noun propositional phrases and 38 from nominal clauses. All these instances account for 55 % of all suo sentence s in Babel. On the other hand, suo sentences in academic prose of translated Chinese (ZCTC) have some particular usage, such as in the passive structure or in a quotation, which appear in the native English subcorpus of Babel but quite few in frequency (there are only 19 for passives and 15 for quotations, accounting for 6 % and 4.8 %, respectively, of all occurrences related to suo sentences). The above discussion of suo sentences in native and translated Chinese shows there is signifi cant difference between the two types of texts. On the one hand, academic prose in native Chinese has more common use of suo sentences; the unique passive construction 为/被…所… wei/ bei….suo… , in particular, is signifi cantly overused in native texts than in translations. On the other hand, in the other three broad registers, suo sentence s are over-represented in translation Chinese, which may be attributable largely to the interference of English nominal structures.

7.6 Lian Constructions in LCMC and ZCTC

As one of the basic sentence patterns in Chinese, the lian construction is used to highlight the focus of a topic, usually consisting of the auxiliary连 lian and adverb 都 dou “all” or也 ye “also/as well”. Dou is used for inclusion of the focus, while ye indicates similarity of the focus to others. For example:

(1) 他们每天三班倒,连大年初一也不例外. Tamen meitian san ban dao, lian danian chuyi ye bu liwai. They everyday three-CL shift, AUX big-year ﬁ rst also not-exceptional. They (work) in three shifts everyday, even if the New Year Day is not exceptional. 7.6 Lian Constructions in LCMC and ZCTC 145

(2) 她连看也不看. Ta lian kan ye bu kan She AUX see/look also not/look She doesn’t even take a look. (3) 东京人连一个铜板也不愿丢给乞丐 Dongjing ren lian yige tongban ye buyuan diu gei qigai Tokyo-people AUX one-CL copper-coin also not-willing throw-give beggar The Tokyo people don’t even want to give one penny to a beggar.

The lian construction is used before the predicate with that aim of supplying necessary background information already known to the speaker and highlighting the subject. An interesting characteristic of this construction is that it implies a comparison between the topic (subject) and other topics usually known as common knowledge among the speakers (cf. Liu and Xu 1998 ). In example (1), the speaker implies a comparison of the New Year Day with other days, because most people agree the New Year Day is special; if the most special day of a year is not exceptional, other days will be out of the question (e.g. of having a holiday). In the lian construction, the focuser lian can be followed by a noun, a verb, a numeral classifi er or even a clause. Note that if lian is put before a verb or a numeral classifi er, the lian construction is usually in negation; see examples (2) and (3). This section analyses and compares the performance of lian constructions in native and translated corpora. Figure 7.10 shows the distribution of lian construction s in the native Chinese corpus (LCMC ) and its translated counterpart (ZCTC ) in normalised frequencies of 100,000 words. It is clear from the fi gure that in both native and translated corpora, lian constructions are most common in fi ction and least frequent in academic prose. Among the four broad registers, translations and native texts only show some differences in fi ction (though the signifi cance level is not statistically meaningful), in which native texts have a slightly higher frequency of lian constructions than translated texts, whereas in other three registers, there are no statistically signifi cant differences between the two types of texts. But in general, lian constructions are more favoured in native Chinese than in translated Chinese, and the difference is statistically signifi cant (LL = 5.95, p < 0.05; see Table 7.6). There are similar expressions in English (e.g. even if , even though); however, the semantic range of even is much wider than the lian construction in Chinese (cf. Lin 2002 ). This has been shown as another diffi culty for English native speakers who learn Chinese. To us, it seems not surprising to fi nd a unique Chinese sentence pattern less presented in translations and over-presented in native texts of Chinese, as this can be explained under the TUs hypothesis of under-representation of the TL unique items . 146 7 The Grammatical Features of Translational Chinese

15 LCMC 10 ZCTC 5

0 A-C D-H J K-R Mean LCMC 15 11 4 27 15 ZCTC 11 9 4 19 11

Fig. 7.10 Distribution of lian constructions in LCMC and ZCTC

Table 7.6 Log-likelihood tests for lian constructions in LCMC and ZCTC Register LCMC ZCTC LL Signiﬁ cance News (A–C) 28 21 1.19 0.276 General prose (D–H) 50 39 1.54 0.215 Academic prose (J) 7 7 0.004 0.950 Fiction (K–R) 73 53 3.43 0.064 Mean (A–R) 158 120 5.95 0.015

7.7 Classiﬁ ers in LCMC and ZCTC

Chinese is well accepted as a classifi er language in which the use of classifi ers is mandatory (with a few exceptions in some idioms, e.g. 九牛二虎之力 jiuniu erhu zhi li , “the power of nine buffalos and two tigers”; 三头六臂 santou liubi , “three heads and six arms”). It has a very well-developed classifi er system which, as it has been estimated, has 500–600 commonly used classifi ers (cf. Guo 1987 : 10). Xiao and McEnery ( 2010 ) indentify eight categories of classifi ers: arrangement classifi er, collective classifi er, measure classifi er, container classifi er, species classifi er, temporal classifi er, unit classifi er and verbal classifi er. These classifi ers comprise three broad categories: nominal (q ), verbal (qv ) and temporal (qt ) (see Appendix 1 for part-of-speech tag set), which are used, respectively, to quantify nominal entities, verbal actions and time (Xiao and McEnery 2010 ). Figure 7.11 shows the normalised frequencies (per 100,000 words) of classifi er s in native and translational Chinese, including their distribution in the four broad registers. It is clear that the overall frequency of classifi ers is higher in native than translational Chinese ( LL = 36.66 for 1 d.f., p < 0.001; see Table 7.7 ). Classifi ers are 7.7 Classifi ers in LCMC and ZCTC 147

3000 2500 2000 1500 1000 LCMC 500 ZCTC 0 General Academic News Fiction Mean prose prose LCMC 2340 2229 1366 2469 2127 ZCTC 1864 2097 1622 2379 2051

Fig. 7.11 Classiﬁ ers in LCMC and ZCTC

Table 7.7 Log-likelihood tests for classifi ers in LCMC and ZCTC Register LCMC ZCTC LL Signifi cance News (A–C) 4,372 3,571 100.00 0.000 General prose (D–H) 9,822 9,386 17.40 0.000 Academic prose (J) 2,334 2,867 37.84 0.000 Fiction (K–R) 6,742 6,573 4.47 0.034 Mean (A–R) 23,270 22,397 36.66 0.000 also signifi cantly more frequently used in native Chinese in all registers other than academic prose, refl ecting the crosslinguistic difference in the status of classifi ers in Chinese and English: English is not a classifi er language, in which the use of classifi ers is only required for noncount nouns, which explains why classifi ers are 29 times as frequent in Chinese as in English (cf. Xiao and McEnery 2010 ). In the register of academic prose, in contrast, classifi er s are signifi cantly more frequent in translational Chinese and the difference is statistically signifi cant (see Fig. 7.11 and Table 7.7 ). A cross tabulation of registers and classifi er categories can help to account for this different distribution pattern. As shown in Fig. 7.12 and Table 7.8, in the news register all three types of classifi ers are more frequent in native Chinese, and log-likelihood tests indicate that these differences are statistically signifi cant (p ≤ 0.001). In general prose, all types of classifi ers are also more frequent in native Chinese, but only the difference in verbal classifi ers (qv) is signifi cant (p < 0.001). In academic prose and fi ction, only the difference in temporal classifi ers (qt) is signifi cant (p < 0.001), with fi ction displaying a more frequent use of classifi ers in native Chinese and academic prose showing a more frequent use of classifi er s in translational Chinese. The cross tabulation in Fig. 7.12 suggests that academic prose alone makes signifi cantly more frequent use of one type of classifi er s, namely, temporal classifi ers (qt). Concordances show that the temporal classifi er that makes the difference is 年 nian “year”, which is mainly used in citing statistics and making references in 148 7 The Grammatical Features of Translational Chinese

2000 1800 1600 1400 1200 1000 800 LCMC 600 ZCTC 400 200 0

Fig. 7.12 Genre variations in the distribution of classiﬁ ers

Table 7.8 Log-likelihood tests for various types of classifi ers in LCMC and ZCTC Registers Classifi er types LL score Signifi cance News (A–C) Nominal classifi er (q) 74.40 0.000 Temporal classifi er (qt) 10.79 0.001 Verbal classifi er (qv) 16.60 0.000 General prose (D–H) Nominal classifi er (q) 2.23 0.136 Temporal classifi er (qt) 3.22 0.073 Verbal classifi er (qv) 19.08 0.000 Academic prose (J) Nominal classifi er (q) 0.036 0.850 Temporal classifi er (qt) 203.18 0.000 Verbal classifi er (qv) 0.755 0.385 Fiction (K–R) Nominal classifi er (q) 0.068 0.795 Temporal classifi er (qt) 23.93 0.000 Verbal classifi er (qv) 0.012 0.912

academic writing, e.g. 根据1997年6月BIS的报告 genju 1997 nian 6 yue de BIS baogao “According to the BIS report in June 1997” and 正像1991年的《世界发展报告》所指出的那样 zheng xiang 1991 nian de 《shijie fazhan baogao》 suo zhi- chu de nayang “As pointed out in World Bank (1991)”. This classifi er alone takes up 80 % of temporal classifi ers in academic prose in the translational corpus, which doubles the corresponding percentage in the same register in the native Chinese corpus. Clearly, the signifi cantly more frequent use of this temporal classifi er in academic prose in translated Chinese texts can be attributable to the more rigorous academic referencing and citation practice in English source texts. Because of this cultural difference, the use of classifi ers in academic prose might be considered as 7.8 Aspect Markers in LCMC and ZCTC 149 a special case, which will not invalidate the fi nding that classifi ers are generally more common in native Chinese. In other words, translational Chinese is characterised by under- representation of classifi er s.

7.8 Aspect Markers in LCMC and ZCTC

Chinese as an aspect language relies heavily on aspect markers to express temporal and aspectual meanings (together with other lexical devices). Different from English which uses morphologically combined tense/aspect markers, Chinese uses lexical aspect markers, i.e. a type of grammatical words, to express aspectual meaning. Explicit aspect markers in Chinese have a relatively complicated system that can be put into two major categories and eight subcategories, namely, (1) perfective aspects, including the actual aspect marked by 了 -le , the experiential aspect marked by 过 - guo, the delimitative aspect marked by verb reduplication and the completive aspect marked by resultative verb complements (RVCs), and (2) imperfective aspects, ranging from the durative aspect marked by 着 - zhe, the progressive aspect marked by 在 zai , the inceptive aspect marked by 起来 - qilai and the successive aspect marked by 下去 -xiaqu . Apart form explicit aspect markers, Chinese also uses implicit markers to express aspetucal meaning (cf. Xiao and McEnery 2004 ). In relation to Chinese, English is a less aspectual language with regard to viewpoint aspect. It only differentiates between the simplex viewpoints of the “progressive”, the “perfect” and the “simple aspect” in addition to the complex viewpoint of the “perfect progressive” (cf. Biber et al. 1999 : 461; Svalberg and Chuchu 1998 ). While the perfective/imperfective dichotomy also applies to English, English does not have a productive morphological distinction between the two. Rather English relies on other grammatical and semantic categories like tense to encode this aspectual distinction. The aspect marker s in English are combined with tense markers morphologically and/or syntactically. For example, past progressive can express both the tense (past) and the aspectual meaning (progressive). In English, perfective meaning is most commonly expressed by the simple past (cf. Brinton 1988 : 52), though the perfect can also mark perfectivity (Dahl 1999 : 34). Imperfective meaning is typically signalled by the progressive and less often by the perfect progressive. The distribution of aspect markers is also different in English and Chinese. In non-literary expository registers, perfective aspect markers are generally more common in Chinese than in English, whereas in literary naratives, perfective aspect markers are more frequently used in English than in Chinese. However, in terms of imperfective aspect marker s, the difference between English and Chinese is inversed: English has more frequent use of imperfective aspect markers in non- literary expositions, while Chinese has more of this type of aspect markers in literary naratives (cf. Xiao 2003 ). These differences are due to the different speciﬁ c discourse functions and usage ranges of aspect markers in both languages (McEnery et al. 2003 ). 150 7 The Grammatical Features of Translational Chinese

1400 1200 1000 800 600 LCMC 400 ZCTC 200 0 Non-literary (A-J) Literary (K-R) Mean (A-R) LCMC 737 1162 845 ZCTC 717 1048 801

Fig. 7.13 Perfective aspect marker 了 -le in LCMC and ZCTC

Table 7.9 Log- likelihood tests for了-le in LCMC and ZCTC Text category Text category LCMC ZCTC LL Non-literary (A–J) 5,881 5,851 2.07 0.151 Literary (K–R) 3,173 2,897 15.82 0.000 Mean (A–R) 9,054 8,748 12.59 0.000

As indicated by McEnery and Xiao’s ( 2002 ) analysis based on a specialised medical and health-care English-Chinese parallel corpus, on the one hand, the ratio between explicit and implicit aspect markers in translated Chinese is very low; on the other hand, the actual aspect marker 了 –le and the experiential aspect marker 过 –guo are signifi cantly less frequent in translated texts than in comparable native Chinese texts. From their study, we see that there are signifi cant differences between translated and non-translated Chinese in the spcicalised subject area of medical and health care. This section investigates the use of three well-established aspect markers, namely, perfective aspect markers 了 -le and 过 -guo and the imperfective aspect marker 着 -zhe , in native and translational Chinese Figure 7.13 shows the normalised frequencies (per 100,000 words) of the perfective aspect marker了 – le in literary and non-literary categories in the two corpora. As can be seen, in general, native Chinese corpus, LCMC , has more common use of 了 –le than translated Chinese corpus, ZCTC . The LL test results in Table 7.9 indicate that the difference has statistical signifi cance (LL = 12.59, 1 d.f., p < 0.001). Given the fact that了 –le is mainly used in narrations, the difference between translated and native texts has stastistical signifi cance only in the literary category (K–R). Although in the non-literary category (A–J), native Chinese has a slightly higher frequency of了 – le than translated Chinese, the difference is not signifi cant statistically (see Table 7.9 ) 7.8 Aspect Markers in LCMC and ZCTC 151

700

600

500

400

300 LCMC ZCTC 200

100

0 Non-literary (A-J) Literary (K-R) Mean (A-R) LCMC 212 628 318 ZCTC 151 583 261

Fig. 7.14 Perfective aspect marker 过 – guo in LCMC and ZCTC

Table 7.10 Log- likelihood tests for 过 guo in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 675 627 2.95 0.086 Literary (K–R) 493 548 2.29 0.130 Mean (A–R) 1,168 1,175 0.100 0.752

Figure 7.14 displays the normalised frequencies (per 100,000 words) of the perfective experiential aspect marker 过 –guo in native and translated Chinese corpora. It is clear that过 – guo is also more frequently used in literary than in non-literary registers. However, because of the low general frequencies of this aspect marker, the differences between native and translated texts are relatively very small and, as shown in Table 7.10 , are not statistically signifi cant. Figure 7.15 gives the similarly normalised frequencies of the imperfective aspect marker 着 – zhe in both LCMC and ZCTC, which indicates that native Chinese has signifi cantly higher frequencies of着 –zhe than translated Chinese whenever the two corpora are contrasted as a whole or, respectively, compared in literary (K–R) or non-literary (A–J) categories. All these differences are statistically signifi cant as shown in the LL tests in Table 7.11 . The above discussion of the distribution of the three aspect markers in the two Chinese corpora suggests that -le and -zhe are signifi cantly more frequent in native Chinese (p < 0.001) though -guo does not show a signifi cant difference (p = 0.752), possibly because of its low overall frequency of use in the two corpora. Hence, it may be concluded that, in general, aspect markers are under-represented in translational Chinese. This conclusion is also in line with McEnery and Xiao ( 2002 ), but extends their observation in a specialised subject area to the Chinese translations in general, that is, the use of aspect markers is indeed a distinctive feature that differentiates native and translated Chinese texts. The aspect markers as discussed in this 152 7 The Grammatical Features of Translational Chinese

250

200

150

LCMC 100 ZCTC

0 Non-literary (A-J) Literary (K-R) Mean (A-R) LCMC 85 181 109 ZCTC 77 198 108

Fig. 7.15 Imperfective aspect marker 着 – zhe in LCMC and ZCTC

Table 7.11 Log- likelihood tests for 着 zhe in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 1,691 1,235 80.36 0.000 Literary (K–R) 1,714 1,611 4.48 0.035 Mean (A–R) 3,405 2,846 61.29 0.000 section can also be taken as a type of auxiliary (i.e. verbal auxialiary), while another type of auxialiary, structual auxiliary, will be analysed and contrasted in this next section.

7.9 Structural Auxiliaries in LCMC and ZCTC

In this section, we will continue to discuss the distribution of structural auxiliaries , i.e. 的 de , 地 de , 得 de and 之 zhi . These function words have no content meaning but are used to join two words or are used to follow a particular word to indicate a certain grammatical structure or relational meaning. More specifi cally, 的 de is the attributive marker that joins two words to form an adjectival endocentric structure functioning as an attributive modifi er. 地 di is the adverbial marker that joins two words to form an adverbial or verbal endocentric structure functioning as an adverbial modifi er. 得 de is the complemental marker that joins two words to form an adjectival or verbal endocentric structure functioning as a complement following a verb. 之 zhi , like 的 de , is also an attributive marker, which is an archaic structural auxiliary handed down from ancient Chinese and used in modern Chinese to form the four-character structures or to replace 的 de to avoid repetition. 7.9 Structural Auxiliaries in LCMC and ZCTC 153

7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Nonliterature Literature Mean LCMC 5,494 4,538 5,250 ZCTC 6,470 5,307 6,176

Fig. 7.16 Structural auxiliaries 的 de , 地 de and 得 de

Table 7.12 Log- likelihood tests for 的 de , 地 de and 得 de Text category LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 43,868 52,786 607.04 0.000 Literary (K–R) 12,394 14,665 157.42 0.000 Mean (A–R) 56,262 67,451 764.63 0.000

Figure 7.16 and Table 7.12 compare the normalised frequencies (per 100,000 words) of the three modern structural auxiliaries in native and translational Chinese. As can be seen, no matter whether the two corpora are taken as a whole or literary and non-literary components are considered separately, the three modern structural auxiliaries are signifi cantly more frequent in translated texts ( p < 0.001). The result may be unexpected given that these words are unique in Chinese and have no formal equivalents in English. However, because these structural auxiliaries are all highly frequent function word s in Chinese, it is words such as these that function to facilitate structural explicitation. In this sense, it is a quite natural expectation for these high-frequency function words to occur more frequently in translational Chinese. This fi nding is also in line with Hu (2010 ) based on GCEPC (see Sect. 4.4.2 ). The archaic structural auxiliary 之 zhi , in contrast, displays a totally different pattern of distribution as shown in Fig. 7.17 and Table 7.13 , which show its normalised frequencies in the two Chinese corpora. While the frequency of 之 zhi is not particularly high, the contrast between native and translational Chinese is marked, with a much more frequent use in native Chinese, both in literary and non-literary texts. The signifi cantly less frequent use of 之 zhi ( p < 0.001) appears to be related to its archaic style: translators tend to consciously avoid archaic words and structures in favour of simpler modern forms (see also Sect. 4.2 ), which might be taken as a manifestation of translational simplifi cation . 154 7 The Grammatical Features of Translational Chinese

120 100 80 60 LCMC 40 ZCTC 20 0 Nonliterature Literature Mean

Fig. 7.17 Archaic structural auxiliary 之 zhi in LCMC and ZCTC

Table 7.13 Log-likelihood tests for auxiliary 之 zhi in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 909 710 31.58 0.000 Literary (K–R) 260 118 56.31 0.000 Mean (A–R) 1,169 819 68.84 0.000

7.10 Modal Particles in LCMC and ZCTC

This section discusses another unique feature of Chinese, namely, modal particles , which are used at the end of a sentence to express the speaker’s mood or attitude. In modern Chinese, the speaker’s mood or attitude, whether indicative, interrogative, imperative or exclamative, is often expressed with tones in spoken Chinese, while completely dependent on modal particles in written Chinese. Commonly used modal particles in Chinese include吗 ma , 呢 ne and 吧 ba, among others. They have no formal equivalents in English, which uses auxiliaries, modal verbs as well as word order and tones to achieve similar effects. Figure 7.18 and Table 7.14 illustrate the distribution of modal particles in native and translational Chinese. The normalised frequencies (per 100,000 words) show that modal particles are signiﬁ cantly more frequent in native Chinese (p < 0.001), in both literary and non-literary texts, suggesting that translational Chinese is indeed different from native Chinese. The less frequent use of modal particles in translational Chinese can possibly be explained by the fact that modal particles are a unique grammatical category in Chinese that is not used in the English source language, thus affecting the use of modal particles in translated texts. In this sense, TL unique item under-representation might as well be regarded as an indicator of source language interference. 7.10 Modal Particles in LCMC and ZCTC 155

1,400 1,200

1,000

800

600

400

Frequency per 100,000 words 200

0 Nonliterature Literature Mean LCMC 372 1,252 596 ZCTC 289 1,122 499

Fig. 7.18 Modal particles in LCMC and ZCTC

Table 7.14 Log-likelihood tests for modal particles in LCMC and ZCTC Text category LCMC ZCTC LL Signiﬁ cance Non-literary (A–J) 2,970 2,355 84.76 0.000 Literary (K–R) 3,420 3,100 19.34 0.000 Mean (A–R) 6,390 5,455 92.59 0.000

In this chapter, we have discussed in great detail the grammatical characteristics of translational Chinese as represented by the basic syntactic constructions and grammatical structures which are unique in Chinese and have been meticulously studied so far. From the above discussion, we can see that these basic constructions and structures do effectively differentiate translational Chinese from native Chinese. These fi ndings are generally supportive of the proposition that translational language is a special linguistic variant which is signifi cantly different from native language and deserves serious investigation in its own right. Although it is diffi cult to fi nd any qualitative differences between native and translated texts, i.e. to fi nd discriminatory linguistic features that are present in one linguistic type but completely absent in another, the actual use of language, particularly stylistic preference, is probabilistic by nature. Therefore, in terms of linguistic features, there are undoubtedly quantitative differences between the two types of language use, translations and native writings. In the next chapter, based on all these detailed empirical and quantitative analyses that we have presented from Chaps. 5 , 6 and 7, we will further explore the connection of these fi ndings with the hypotheses of translation universals . Chapter 8 The Features of Translational Chinese and Translation Universals

We have so far analysed and compared translational and non-translational or native Chinese as represented by our corpora LCMC and ZCTC in terms of their macro- statistic features in Chap. 5 and the lexical and grammatical characteristics in Chaps. 6 and 7 , while the present chapter is an interface between the empirical fi ndings and theoretical hypotheses, that is, it is a combination of descriptive translation studies with the “pure translation theory” (Holmes 1972 /1988). It is important to fi nd these connections for the reason that without a higher level of generalisation, empirical and quantitative discoveries can be meaningless or aimless. We will fi rst of all summarise the discriminatory features of translational Chinese at different levels and then discuss the implications, if any, of these translation specifi c features to translation universals hypotheses reviewed in Chap. 3 . Due to the fact that the translated corpus (ZCTC) used as the basis of this research consists mostly of translated texts from English and that the parallel corpus (GCEPC) which is used whenever necessary is a corpus of English and Chinese translation, our generalisation for the sake of translation universals should be limited within the particular realm of English-to- Chinese translation.

8.1 The Macro-Statistic Features of Translational Chinese

As discussed in Chap. 5 , the contrastive analyses of native and translated Chinese texts based on our balanced comparable corpora demonstrate a lower lexical density in translational Chinese than in native Chinese; in other words, the textual information load of translated Chinese texts is lower than native texts. This is largely due to the ratio of content words against function words in translated Chinese which is signiﬁ cantly smaller than in native Chinese. The decrease of textual information load indicates a tendency of semantic simpliﬁ cation, while the increased proportion of function words in translated Chinese can be taken an indication of grammatical explicitation.

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 157 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_8 158 8 The Features of Translational Chinese and Translation Universals

Alternatively lexical density can be measured in terms of standardised type/ token ratio ( STTR ). The respective data of STTR in LCMC and ZCTC shows that the lexical variability of translational Chinese is slightly lower than native Chinese irrespective of literary or non-literary text categories; nevertheless, these differences are not statistically signifi cant. This can be interpreted as an indicator suggesting that there is no substantial difference between the two types of language use, translation and native writing, although native writing is probably more variable than translation in the choice of vocabulary. The analyses of the word lists of both corpora show that, in terms of tokens, translational Chinese presents a preference for high-frequency words (tokens), though in terms of types, translational Chinese uses fewer types of high-frequency words. In other words, translated Chinese tends to repeatedly use a smaller number of high-frequency words. There is also evidence that the low-frequency words in translated Chinese outnumber those in native Chinese; in addition, translational Chinese prefers longer words than non-translational Chinese. The super-high-frequency words on the top of a word list are usually the most fundamental lexicon in a language; and most of them are function words with grammatical or structural functions. Repeated use of such high-frequency words may indicate a tendency of lexical simplifi cation in translated Chinese, while on the other hand, it can be also taken as an indication of grammatical explicitation. However, simplifi cation in translational Chinese may not be as simple as it appears: although the translator may try to simplify the translation by repeating commonly used words intentionally or unintentionally, the actual result of this can be quite unexpected because of various factors across languages and cultures that translating involves. For example, it is inevitable to use transliteration words (for proper names) in English-to- Chinese translation, which results in longer mean word length in translated Chinese; there are more word types that are not repeated in translation than in native writing. Additionally, there are more low-frequency words in translated Chinese. These factors will increase the actual diffi culty of reading. Therefore, we cannot conclude that all the linguistic features in Chinese translation are meant for simplifi cation, not even that translated texts are lexically simpler than native texts. In terms of mean sentence and paragraph length, translational Chinese in general uses slightly shorter sentences and paragraphs, but these differences are actually normal fl uctuations rather than substantial discriminations. However, there are greater register variations of sentence and paragraph length between the two types of language use. Specifi cally, across the four major registers, i.e. news, general prose, academic prose and fi ction, it is only in fi ction that the mean sentence length in native Chinese is signifi cantly greater than in translated Chinese and that the mean paragraph length of translated Chinese is signifi cantly shorter than native Chinese. That is to say, in all other three major registers, translated Chinese has longer sentences but shorter paragraphs than native Chinese. As for mean sentence segment length , with the exception of fi ction where the two types of texts have no statistical signifi cant differences, in all other three registers (particularly in academic prose), translated Chinese has signifi cantly longer mean sentence segment length than native Chinese. 8.1 The Macro-Statistic Features of Translational Chinese 159

In actual translating, while it is normal to break a sentence in a source text into segments and reorganise them in a target text, paragraphs are usually kept as they are in the source text. So the differences in mean paragraph length between translation and non-translation are rather a refl ection of the interlingual differences between the source language and the target language , although it may be pointless to directly compare paragraph length of genetically different languages. As Chinese is a parataxis language, the relationships among sentential components are often “inter- nalized, implicit or ambiguous” (Liu 1991 : 158); despite being explicitly marked by sentential-ending punctuation marks, a Chinese sentence is very likely to be made of several sentence segments which are semantically complete and grammatically independent. As a result, mean sentence segment length (MSSL) is more meaningful than mean sentence length for comparisons of translated and native Chinese (Wang and Qin 2010 ). The longer mean sentence segments in translated Chinese based on our corpora may indicate that, on the one hand, translational Chinese is infl uenced by English as the main source language which usually has longer sentences (i.e. as source language interference) and on the other hand, this can also be the result of translation-induced explicitation in cohesion and coherence. Furthermore, the fact that translated Chinese has longer MSSL than native Chinese in all three main registers barring fi ction also casts new light on our understanding of the simplifi cation TUs hypothesis. As a basic unit of reading at the sentential level, longer mean sentence segments in translated Chinese texts may require more time of reading and, therefore, may not be easier to read than native Chinese texts with shorter MSSL. However, if the increase of MSSL is due to translation explicitation, the lexical and phrasal structure within a sentence segment is more explicit, resulting in an easier reading of the sentence segment. As such, there are some properties in the translated texts that help to simplify the texts, while, in the mean time, there are other properties that may result in more complicated texts. The simplifi cation hypothesis, therefore, is in fact testifi ed by a mixture of evidence and counter evidence. What is more reasonable is probably not to verify or deny the simplifi cation hypothesis in an absolute sense but to consider and compare various factors, supportive or subversive, to reach at a more detailed and hence more profound understanding of simplifi cation. Analyses of two- to six- word clusters in the two Chinese corpora show that word clusters, particularly high-frequency word clusters, exhibit signifi cantly higher frequencies in translated Chinese in relation to native Chinese. In the meantime, the overall and highest coverages of word clusters in translations outnumber native writings. In addition to quantitative differences, word clusters in translated Chinese may possess qualitative differences in contrast to native Chinese. For example, the high-frequency word clusters made of two to three words in translations are mostly modifi ers (as in a phrase), while similar word clusters in native texts are central structures (i.e. as the core of a phrasal construction). High-frequency word clusters are closely related to the formulaic usage of language, which helps to improve the fl uency and idiomaticity of an expression (Wray 2002 ); for this reason, they can be taken as a measure for the degree of idiomaticity of translated texts (cf. Xia and Li 2010 ). On the one hand, the evidence of more 160 8 The Features of Translational Chinese and Translation Universals occurrences of high-frequency and high-coverage word clusters in translated Chinese may indicate that the translators are more conscious in producing translations which are more fl uent and more conformable to TL norms of expression. Although this phenomenon is usually explained in terms of the normalisation TUs hypothesis in the literature (e.g. Baker 2007 ), it is actually more convincing to be put under the simplifi cation hypothesis: more fl uent and more idiomatic translations compensate to a greater or less extent the diffi culties of reading due to larger numbers of long words and low-frequency words used in them. Given the apparent structural differences between the language systems of SL and TL, it can be para- doxical for explanations from the normalisation perspective, that is, it is diffi cult to tell the differences between compulsory translation shifts due to system discrepancies and optional translation normalisation made by the translator. On the other hand, the preference for high-frequency and high-coverage word clusters in translated Chinese may also be interpreted as an indication of source language interference , because as the comparable data from FLOB and LCMC shows, word clusters in native English have much higher usage frequencies and larger coverage rates than native Chinese. Last but not least, the series of qualitative and quantitative differences between translated and native Chinese texts also demonstrate relative similarities between translated texts themselves, which can be said to represent a tendency of levelling out or convergence of translations.

8.2 The Lexical Features of Translational Chinese

Chapter 6 contains detailed discussions of the lexical features of translational Chinese. As the analyses of keyword lists reveal, the frequencies of commonly used function words, particularly auxiliaries, pronouns, conjunctions and prepositions, are signifi cantly higher in translated Chinese. Note that the overused auxiliaries mainly include structural auxiliaries (i.e. 的 de , 所 suo , etc.) with the exception of verbal auxiliaries (i.e. aspect markers), such as了 -le , 着 - zhe and 过 -guo ; and overused prepositions should also exclude the special proposition of把 ba as in ba constructions. Analyses of negative keyword lists show that translated Chinese uses fewer lexical items which are unique in Chinese, such as emotional and oral lexicons (e.g. 咱 zan “I”), dialectal lexicons (e.g. 啥 sha “what”), archaic lexis (e.g. the structural auxiliary 之 zhi GEN) or generalised verbs (e.g. 搞 gao “do”, 办 ban “do, make”). In the meantime, instead of employing a wider range of synonyms or near synonyms as in native Chinese, translated Chinese tends to use high-frequency or commonly used words repeatedly. Analyses of key word class es have shown that in addition to the overuse of the above four word classes, translated Chinese demonstrates much heavier reliance on suffi xes (e.g. 们 men , 者zhe, 症 zheng, 型 xing , 化 hua , 率 lv, etc.), dummy or generalised verbs (e.g. 进行 jinxing “proceed”, 给予 jiyv “give”, 予以 yuyi “give”), the passive marker (被 bei ), the judgemental verb (是 shi “is”) and the punctuation of brackets. On the other hand, analyses of negative key word classes demonstrate that translated Chinese displays a less strong 8.2 The Lexical Features of Translational Chinese 161 preference for content words such as nouns, verbs and adjectives, special verb 有 you and idioms in nominal or verbal forms. In addition, translated Chinese also uses fewer Chinese-specifi c linguistic features, such as modal particles, full-length enumeration marks, aspect marker着 - zhe and auxiliaries like 等 deng , 等等 dengdeng , 云云 yunyun “etc. and so on” and 之 zhi GEN. Contrastive analyses of pronoun s in both corpora show that the distribution patterns of various types of pronouns are similar in translated and native Chinese, with personal pronoun s as the most frequent type and interrogative pronoun s as the least used type. Nevertheless, translated Chinese appears to prefer personal pronouns, because the total frequencies of personal pronouns in translated Chinese are signifi - cantly higher than those in native Chinese by 50 %. The general tendency of translated Chinese in using fewer content word s but more function words such as auxiliaries, pronouns, conjunctions and prepositions may be interpreted as a reduction of textual information load and hence an indication of semantic simplifi cation; repetition of high-frequency words and avoidance of a wider range of near synonyms may result in smaller vocabulary required for reading, which can be taken as a means of lexical simplifi cation . Additionally, heavier reliance on function words can help to explicate the grammatical structure of a text. This is particularly true for personal pronouns and demonstrative pronouns because of their connective discourse function. The more frequent use of the two types of pronoun s helps to improve the cohesion and coherence of text. On the other hand, the overuse of pronouns may also be an indicator of source language interference in that the increased use of pronoun s might be the subject of a sentence retained in translation under the infl uence of the source text. The subject of a Chinese sentence normally has a very strong connective function, due to which, in native Chinese, once used in the fi rst clause or sentence segment, the subject is usually omitted or implicated in the following segments. This is of course not possible in hypotaxis or infl ectional languages (e.g. English). The over-representation of connectives and brackets in translated Chinese can also be taken as indicating semantic explicitation of the internal logic relation within a sentence. Furthermore, higher frequencies of suffi xes, bei passive constructions and dummy words may come from the shining-through effect or interference from the source language . In contrast, some of the unique features of Chinese are under-represented in translation; this tendency seems to go against the normalisation hypothesis. Further analyses of the distribution patterns of word classes confi rm the above fi ndings with regard to keyword s and key word classes: translated Chinese tends to have fewer occurrences of content word s and some Chinese-specifi c word classes such as classifi ers and modal particle s; in contrast, it has increased numbers of function words, i.e. auxiliaries, pronouns, prepositions and conjunctions. This tendency is also clear in analyses of word-class clusters. For example, word-class clusters consisting of nouns, verbs and adverbs are usually ranked higher in native Chinese than in translated Chinese, while word-class clusters made of structural auxiliaries and personal pronouns are signifi cantly more frequent in translated Chinese. Most high-frequency and high-coverage 3-word word-class clusters in translated Chinese contain function words, whereas similar clusters in native Chinese are mostly made 162 8 The Features of Translational Chinese and Translation Universals of content words. In the meantime, most key word-class clusters in translation have function word classes; and most negative key word-class clusters consist of content word classes. In terms of the behaviour of conjunction s, in addition to their signifi cantly higher frequency, translated Chinese shows discriminatory characteristics in the distribution of conjunctions of various frequency bands and styles. On the one hand, high- frequency conjunctions are more frequent in Chinese translations, while low-frequency conjunctions are more frequent in native Chinese; on the other hand, informal, oral and simple conjunctions are more preferable to Chinese translations; by contrast, formal and even archaic use of conjunctions tends to appear in native writing. The study of distribution patterns of word class es and word-class clusters provides evidence for our previous fi ndings that translational Chinese has lower textual information load but higher level of grammatical explicitation . The under- representation of some Chinese-specifi c features (e.g. classifi ers and modal particle s) is presumably related to the interference of the source language. Moreover, the tendency of using more simple conjunctions can be taken as supportive to translational simplifi cation . In terms of punctuation use, although there is no apparent difference between the two types of texts in overall frequencies of sentential-ending punctuation marks, translated Chinese shows more reliance on full stops, while native Chinese has a larger variability of exclamation marks, question marks, semicolons and full stops. In general, commas and enumeration marks are less often used in translation. The overuse of full stops and underuse of commas in translated Chinese may be an indicator of transfer from the English source texts; in contrast, native Chinese is inclined to use several commas in one sentence. The enumeration mark is a unique Chinese punctuation mark which is regularly replaced with the English comma. The underuse of enumeration marks may refl ect the fact that the translator tends to directly transfer English commas into the Chinese translation without regard to the conventional use of enumeration marks in native Chinese texts. Finally, dashes and brackets also have signifi cantly higher frequencies in translated texts than native writing. Unlike words, punctuation mark s do not have substantial meaning but may function as clues for differentiating translation from non-translation. Specifi cally, over- reliance on full stops while sacrifi cing variability in the use of other punctuation mark s may be regarded as an indication of the levelling out or convergence tendency of translated texts. Higher frequencies of dashes and brackets can be taken as a translational explicitation. It is also interesting to see Chinese translation tends to follow the SL (English) norms in the use of full stops for sentences instead of using commas according to the TL (Chinese) norms. This obviously refl ects the interference of English in translational Chinese. Reduction of enumeration marks in translations can be understood either in terms of TL unique items under-representation or in terms of SL interference. However, this difference seems to go against the normalisation hypothesis. As discussed previously, high-frequency word clusters can be used to measure the fl uency and conformity of a translated text to the TL norms. Nevertheless, 8.3 The Grammatical Features of Translational Chinese 163

high- frequency word clusters do not necessarily have complete structures or meanings. So another linguistic feature that can truly refl ect the fl uency and idiomaticity of translation is the use of idiom s. Empirical data has shown that idioms are signifi cantly underused in translated Chinese across all four broad registers. This indicates that although a higher ratio of high-frequency clusters may help to improve the fl uency of translation, the lack of idioms in translations reduces their authentic idiomaticity in contrast to the much more frequent use of idioms in native texts. The underuse of idioms is obviously an example of under- representation of TL unique item; but it also means that translated Chinese does not show the inclination to over-representing unique items of the target language as expected under the normalisation hypothesis. Baker ( 2007 ) attempts to explain the performance of idioms in translational English by applying the normalisation hypothesis but gets a para- doxical conclusion: on the one hand, she expects the translator to avoid innovative usage and rely on fabricated or semi-fabricated collocations which are repeatedly used in the target language (Baker’s “idiom” seems more suitable to be called “ word clusters ”); on the other hand, because idioms in English are more often used in an informal style, she also anticipates the translator to avoid idioms, especially those characterised with a high degree of opacity, in order to appear more formal. It appears very diffi cult to generalise from the behaviour of idioms in translated Chinese within the normalisation hypothesis. As a special habitual means of expression, reformulation markers are signifi - cantly more frequent in translated Chinese than in native Chinese; and different styles of reformulation markers are preferred by translation and non-translation. In spite of the higher frequencies of both literate and oral reformulation markers in translations, the former type has no signifi cant difference in both corpora, whereas oral reformulation markers are far more frequent in translated Chinese than the other. Interestingly, both styles of reformulation marker s are also frequent in translational English. As reformulation marker s are an important device of semantic explicitation in translating, the over-representation of these markers in translated texts offers a new support to the explicitation hypothesis. In English-to-Chinese translating, the explicitation rate of reformulation markers ranges from 10 to 30 % in general; nevertheless, the explicitation rates of various reformulation markers vary considerably according to the semantic contents of particular markers.

8.3 The Grammatical Features of Translational Chinese

Chapter 7 compares a number of basic sentence constructions and unique grammatical structures in translated and native Chinese texts in order to provide a comprehensive description of the grammatical features of translational Chinese. As the analyses have revealed, 被 bei passives are more frequently found in translation than in non-translation. The differences in frequency are more signifi - cant in non-literary genres, particularly in formal and written genres such as reports 164 8 The Features of Translational Chinese and Translation Universals and offi cial documents (H), news reviews (C) and academic prose (J). Despite similar higher frequencies of short passives over long passives in both corpora, short passives are much more frequently used in translation than in the other. Furthermore, the occurrences of passives with negative semantic prosody are fewer in translated texts than in native texts, yet occurrences with neutral prosody are signifi cantly greater in translation. The above summary of the differences in bei passives in translated and native Chinese can be largely attributed to the interference of English as the major source language. As shown in the literature, passive structures are pervasive in English so that the frequency of passives in English can be ten times as high as in Chinese (Xiao et al. 2006 ). There is an inclination of overusing passives in such formal and literate genres as offi cial documents, news reports and academic prose in English, due to the particular function of English passive structures to mark an impersonal, objective style. In this sense, the source language interference hypothesis is quite helpful to explain the reasons why, in English-to-Chinese translating, translated Chinese texts have a higher frequency of bei passives than native Chinese texts, particularly in formal-styled genres. Similarly, the more frequent use of short passives in translated Chinese and the changes in pragmatic meaning of passive structures are explicable to SL interference . On the one hand, English makes much more common use of short passives which, when translated into Chinese, tend to be directly transferred into Chinese short passive s. On the other hand, English passives do not have an “infl ictive” connotation which is usually bound with Chinese passives; rather, it has more neutral meanings than negative meanings. This can be used as an explanation for the semantic distribution of positive, neutral and negative meaning categories in translated Chinese which shows respective frequencies of the three types of semantic prosody in native Chinese and English. The analysis based on our English-Chinese parallel corpus also shows that 85 % of bei passives in Chinese translations are directly or indirectly retraceable to passive structures in English source texts; meanwhile, the distribution of passives across registers indicates the interference from English into Chinese passives is more signifi cant in non- literary text categories than in literary registers. The disposal 把 ba construction is also a unique feature of Chinese. The general frequency of this construction in native and translated corpora does not show signifi cant difference; the distribution of ba constructions across registers, though, has great variations. In spite of similar distribution pattern across text categories or registers, i.e. ba constructions are more common in literary texts than in non-literary texts, translational Chinese and native Chinese are distinct in the distribution of ba constructions. Specifi cally, in the literary category, translated Chinese has more frequent use of ba constructions than native Chinese; in contrast, in the non-literary categories, translated Chinese uses fewer ba constructions than native Chinese. This variation seems attributable to the semantic properties of the ba construction, that is, this construction is mostly descriptive and narrative by nature, which is more often used in literary texts. As the ba construction is a colloquial feature and has the textual function of cohesion at the discourse level, when the ba construction is no longer a compulsory 8.3 The Grammatical Features of Translational Chinese 165 translation shift due to systematic differences between SL and TL but an optional choice for the translator, it can be used to make the text easier to read. The more frequent use of ba constructions in translated fi ction, which is for light reading, can be considered as an indicator of simplifi cation. When a register is not for light reading, e.g. in news and academic writing, ba constructions are under-represented in translation. The existential 有 you sentence is another fundamental and unique sentence structure in Chinese, which is similarly distributed across registers in both native and translated corpora, with fi ction having the highest frequency, followed by general prose, news and academic prose. This distribution pattern is related to the descriptive function of the existential you sentence. The frequency of you sentences is signifi cantly lower in translated Chinese than native Chinese as a whole as well as across three registers other than news (the difference between translated and native texts does not have statistical signifi cance in the news register). As the existential you sentence is not only unique in Chinese but also a complicated and semantically abundant structure, the less frequent use of you sentences in translated Chinese may be taken as a tendency for simplifi cation on the one hand and a refl ection of under- representation of TL unique items on the other. The 是 shi sentence is a classic judgemental sentence pattern in Chinese. The signifi cant over-representation of shi sentences in translated Chinese than in native Chinese is largely explicable to source language interference from English. This is because the relationship between the word class and its syntactic function is not as close in Chinese as it is in English. Adjectives, and even nouns, can be directly used as the predicate in a Chinese sentence, whereas adjectives and nouns must be led by the copula be in English. As the empirical analysis has shown, copula be sentences in English are often directly transferred into Chinese shi sentences, regardless of the fact that the shi sentence is restricted to the marked use of emphasis. The conclusion is supported by the fi ndings from the English-Chinese parallel corpus Babel , where more than one-third of “ be + adjective” constructions in the English source texts are translated into the marked shi sentences. Translational Chinese, generally, has a higher frequency of 所 suo sentences than native Chinese, but there are also register variations in terms of the differences between translated and native texts: in translated Chinese, suo sentence s are most common in general prose, followed by academic prose and fi ction; in contrast, fi ction has the least presentations of suo sentences in native Chinese, whereas academic prose has much more frequent use than the other two registers, news and general prose. Therefore, in terms of register variation, suo sentence s are more common in native academic prose than the translated counterpart; but in the other three registers, suo sentences have higher frequencies in translated texts than in native texts. These register variations of suo sentence s is related to the two basic functions of the suo sentence. First, the over-representation of suo sentences in native Chinese academic prose is probably attributable to the stylistic property of the archaic passive construction, 为/被…所… wei/ bei …. suo, which is normally used in the formal and literature style. The underuse of these passive sentences in translated 166 8 The Features of Translational Chinese and Translation Universals

Chinese can be interpreted as the under-representation of this unique sentence pattern. Second, the non-passive suo sentence s are more frequently used in translated Chinese than native Chinese in all other three registers. This is mainly due to the transfer/interference from English particular features (e.g. relative clauses, non- defi nite attributive clauses, prepositional phrases and WH-nominal clauses). In general, 连 lian constructions are more common in native than in translated Chinese, but due to low actual frequency of this construction in both corpora, there are no statistically signifi cant differences between translations and native texts in three registers other than fi ction. The underuse of lian constructions in translated Chinese is largely attributable to under- representation of TL unique items. As a unique grammatical feature of Chinese, the overall frequency of classifi er s is higher in native than translational Chinese, while there are register variations of the difference. Classifi ers are more frequently used in native Chinese in all registers (news, general prose and fi ction) other than academic prose. Our detailed investigation appears to suggest that the overuse of classifi ers in translated Chinese is due to the super-high frequency of one particular classifi er, 年 nian “year”, which is mainly used in citing statistics and making references in academic writing. 年 nian alone takes up 80 % of temporal classifi ers in academic prose in the translational corpus, which doubles the corresponding percentage in the same register in the native Chinese corpus. Clearly, the signifi cantly more frequent use of this temporal classifi er in academic prose in translated Chinese texts can be attributable to the more rigorous academic referencing and citation practice in English source texts. Because of this cultural difference, the use of classifi ers in academic prose might be considered as a special case, which will not invalidate the fi nding that classifi ers are generally more common in native Chinese. In other words, translational Chinese is characterised by under- representation of classifi er s. Chinese is an aspect language in that it uses aspect markers to express tense and aspect. The actual perfective aspect marker了 –le is more common in native Chinese than in translated Chinese, particularly in literary narratives that have relatively higher frequency of this aspect marker. Similarly, the experiential perfective aspect marker 过 –guo is also more frequent in native Chinese than in translated Chinese, but the difference is not statistically signifi cant due to the relative low frequency of this marker. The imperfective aspect marker 着 –zhe is more commonly used in native texts than in translated texts in both literary and non-literary registers. Therefore, we can conclude that aspect markers are generally under-represented in translational Chinese. As aspect markers are unique in Chinese, this phenomenon provides evidence for the TUs hypothesis of target-language unique item under- representation . Structural auxiliaries 的 de , 地 de and 得 de are, respectively, the syntactic markers for the attributive modifi er, the adverbial modifi er and the complement of a sentence, which form a very special characteristic of modern Chinese, while they are also function word s with super-high frequency (的 de is the most frequent word in modern Chinese). No matter it is in literary or non-literary texts, translational Chinese demonstrates a far more marked inclination for these unique super-high- frequency function words. In contrast, another structural auxiliary , 之 zhi , an archaic 8.3 The Grammatical Features of Translational Chinese 167 equivalent of的 de , which is far less frequent in modern Chinese, has a much higher frequency in native than translational Chinese. As structural auxiliaries function to express grammatical relation rather than actual meaning, the overuse of these words in translated Chinese is similar to the overuse of the optional that following a reporting verb in English (cf. Chap. 3 ). This phenomenon can be explained with the translational explicitation of grammatical and structural relation within a text. On the other hand, the underuse of archaic structural auxiliary之 zhi GEN in translated Chinese is probably because the auxiliary is not a high-frequency word, and in this sense, we can interpret this in terms of the TL-specifi c items under-representation hypothesis. In the meantime, the less frequent use of these archaic words from ancient Chinese (e.g. 之 zhi and the passive structure为…所 wei…suo ) can help to make translated texts easier to read. Finally, as a unique Chinese grammatical device, modal particles are more frequently used in native Chinese. This is again related to the under- representation of target- language -specifi c items. However, with the exception of interrogative pronoun s, which are required in specifi c contexts, most other modal particles are optional rather than compulsory. Therefore, the underuse of most modal particles in translated Chinese may be due to the interference of the source language , English, which has no such particles. As has been reviewed and discussed so far in this chapter, we try to supply the reader with a comprehensive summary of the empirical fi ndings from our research with regard to the macro-statistic, lexical and grammatical characteristics of translational Chinese in relation to native Chinese. However, more importantly, we expect to connect all these descriptive fi ndings to theoretical generalisations under the hypothetical translation universal scheme. As we have demonstrated to the reader, there are indeed some interesting relations between the descriptive and theoretical perspectives, which put the numerous detailed discussions of quantitative and qualitative differences between translational and non-translational Chinese under an illuminating explanatory framework of translation studies. We will, in the following chapter, continue to explore the signifi cance of these connections to translation studies and the future possibilities and directions of the research. Chapter 9 Conclusive Remarks

In this fi nal chapter, we will make some conclusive remarks about our research carried out during the past few years. First of all, based on all the distinct linguistic features of translational Chinese as discussed and analysed throughout the book, we will try to re-evaluate the translation universals hypotheses from the Chinese perspective. Evidence and counterevidence for explicitation, simplifi cation, source language shining through , under-representation of TL unique items , convergence and normalisation will be considered altogether for a more comprehensive picture of translational Chinese as one of the “ Third Code s”. We will then refl ect on the major contributions of our research to the studies of translation universals and to descriptive translation studies in general and fi nally close the discussions by exploring some potentially fruitful avenues for future research in this area.

9.1 Translation Universals Hypotheses Re-evaluated from the Chinese Perspective

As has been discussed in the previous chapter, we expect to build the links of the empirical ﬁ ndings of this book to the theoretical generalisations within translation studies. So what can we generalise or learn about translating per se from the Chinese perspective? This question is so important that it would be pointless or meaningless without a profound theoretical interpretation of the descriptive evidence. In this section, we will re-evaluate the TUs hypotheses discussed so far in the face of evidence arising from translational Chinese, on the basis of the lexical and grammatical properties of translational Chinese explored in the previous chapters. As noted before, explicitation has been investigated extensively and has been found to be one of the least controversial TUs hypotheses; it is supported by fresh evidence from translational Chinese in the present study. Explicitation in English- to- Chinese translation is manifested in three aspects: semantic explicitation ,

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 169 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English-Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6_9 170 9 Conclusive Remarks

grammatical explicitation , and logical explicitation . Semantic explicitation means that meaning is expressed more explicitly in translational Chinese by making more frequent use of reformulation marks (Xiao 2011 ) and explicating and explanatory punctuation marks such as dashes and parentheses. Linguistic properties that provide evidence in support of grammatical explicitation include more frequent use of function word s such as pronouns, prepositions and structural auxiliaries in translational Chinese, which all function to explicate grammatical and structural relationships, thus resulting in longer sentence segments. In addition, logical explicitation at the discourse level is evidenced by a higher frequency of conjunction s in translational Chinese, which helps to explicate logical relationships between clauses and make translated texts more cohesive. All of these lexical and grammatical properties of translational Chinese suggest that the English-based hypothesis of translational explicitation also holds in translational Chinese. Of course, explicitation and implicitation in translation are relative phenomena as the extent of explicitation or implicitation is not only affected by linguistic factors but also depends on individual translators’ preferences as well as sociocultural factors (Wang 2005 ). Translational Chinese also has many properties that provide evidence in support of the simplifi cation hypothesis. For example, a lower percentage of content word s in translational Chinese entails a lower information load, which is an indicator of semantic simplifi cation. The tendency in translational Chinese to repeat high- frequency words and colloquial conjunction s ( Xiao 2010b ) and oral reformulation marks (Xiao 2011 ) and to avoid using near synonyms alternatively can be taken as a manifestation of lexical simplifi cation . The less frequent use of syntactically complex and archaic structures in translational Chinese is an indication of grammatical simplifi cation. The more frequent use of word clusters of high frequency and high coverage in translated texts (see Xiao 2011 ), together with the various explicitation strategies as noted above, results in simplifi cation at the discourse level. Nevertheless, translational simplifi cation is actually not such a simple phenomenon. Translational Chinese can be said to be a mixture of simplifi cation and complication, meaning that while some linguistic properties of translations are simpler than comparable original Chinese writings, others may make translated texts even more complicated or hard to read. For example, while translational Chinese tends to repeat ultra-high-frequency words, it also makes less frequent use of sub-high- frequency words in terms of word types but makes more frequent use of hapax legomena and other words of extremely low frequency. The mean word length is generally greater in translated texts than in native Chinese texts, which is unavoidable given that transliterations, which are usually longer than native Chinese words, are abundant in translation. In addition, although no signifi cant difference is found in the mean sentence lengths of native and translational Chinese, the latter has longer sentence segments while as noted earlier, mean sentence segment length is more meaningful than mean sentence length for Chinese as a language characterised by parataxis. Furthermore, how to interpret sentence segment length is another issue. As noted previously, mean sentence length can be interpreted differently. A shorter sentence 9.1 Translation Universals Hypotheses Re-evaluated from the Chinese Perspective 171 may mean a less complex sentence structure, but it can also mean a more compact and less explicit structure that is even more diffi cult to understand; conversely, a longer sentence is likely to be a syntactically complicated sentence that is not simple to read, but it is also possible that the sentence is longer because additional words have been used in the translation to make explicit what is implicit in the source text, thus making the translated message easier to understand. The same can be said of mean sentence segment length in translational Chinese. Hence, it can be concluded that translational simplifi cation is not a pure, simple phenomenon. Some aspects of translational language can be simpler than those in native language, while other aspects may be even more complicated than those in native language. Hence, the TUs hypothesis of simplifi cation should take account of the overall synthetic result of the dynamic interplay between simplifi cation and complication. As Chinese and English are genetically distant languages that have many striking cross-linguistic differences (cf. Xiao and McEnery 2010 ), unique linguistic features in Chinese can help to differentiate translational Chinese from native Chinese in English-to-Chinese translation, if the TUs hypothesis of TL unique item under- representation is tenable. As noted earlier, unique items in Chinese such as Chinese idioms, pause marks, disposal ᢺ ba constructions, aspect markers and modal particles all demonstrate a tendency of under-representation in translational Chinese, despite register variations for some of the features. In addition, while the structural auxiliary Ⲵ de as a highly frequent function word is more common in translational Chinese, the archaic form with a similar function, ѻ zhi, is under-represented in translated texts. Likewise, while the bei passive is more frequently used in translation, possibly because of source language interference, the archaic passive form wei…suo is under-represented in translational Chinese. It is clear that as far as translated texts in English-to-Chinese translation are concerned, the more characteristic a linguistic feature is of Chinese, the better it can serve to differentiate between native and translational Chinese. It appears that the hypothesis of TL unique item under-representation has not received adequate attention in TUs research. This limitation in the status quo seems to be due to the fact that TUs research has largely been based on and confi ned to closely related European languages, in which some linguistic features may not be as markedly dissimilar as in genetically distinct languages such as English and Chinese. While translating can certainly convey messages between languages, given structural differences between languages and underlying cultural differences, a translated text that uses the target language to describe what takes place in the SL culture can hardly achieve the same level of naturalness as a non-translated text that uses the native language to describe an event in its own culture. This unnaturalness is translationese , which is attributable to SL interference and TL unique item under- representation as noted above. Translational language can be said to be a mixture of these two translation phenomena (cf. Teich 2003 ). In English-to-Chinese translation, SL interference has been observed in a range of linguistic features. For example, translational Chinese follows the SL norm of using full stops instead of using commas to break up sentences into sentence segments as in native Chinese, thus resulting in greater mean sentence segment length. As transliterations are inevitable 172 9 Conclusive Remarks in translation, the mean word length is generally greater in translational Chinese. Similarly, the preference for using high-frequency, high-coverage word clusters in translational Chinese is also a result of SL interference . Lexical properties such as the more frequent use of prefi xes and suffi xes, pronouns and light verbs also refl ect the infl uence of the English source language. At the grammatical level, the whole range of differences demonstrated by the bei passive between native and translational Chinese, as discussed in Chap. 7 , are all manifestations of SL interference . Normalisation is a highly debatable TUs hypothesis. Since it was put forward by Baker ( 1996 ), a number of translational features have been used to espouse this hypothesis. Baker ( 2007 ) herself uses idiom s to illustrate her point, but unfortunately she observes two confl icting tendencies in using idioms in English translations. On the one hand, idioms are supposed to be used heavily in translation to conform to the norm of the target language, while on the other hand, idioms, especially those characterised with a high degree of opacity, are expected to be avoided in translation because of their informal tone. Evidence arising from the use of Chinese idioms does not support normalisation either (Xiao and Dai 2010c ). The distribution of pause marks, commas and sentence-fi nal punctuation marks in translational Chinese also invalidates the claim of using stronger punctuation mark s in translation to replace weaker ones in source texts as evidence in support of normalisation. In addition, the preference in translation to repeat high-frequency word clusters or multiword units arguably serves well as evidence in support of simplifi cation than normalisation. More importantly, the discussions of TL unique item under- representation and SL interference show that the differences between native and translational languages are systematic rather than random and occasional. Such systematic differences render translational language a Third Code that is different from both source and target language s. Even though in literary translation, the dialects spoken by different characters in the source texts may be translated into standard target language (see Sect. 2.3), this limited evidence, which might as well be viewed as a means of simplifi cation, is inadequate for such a strong claim that translational language is characterised by “a tendency to exaggerate features of the target language and to conform to its typical patterns” (Baker 1993 : 183), because a wide range of distinctions between native and translational languages cannot be explained away easily by the normalisation hypothesis. In sum, translated texts in Chinese share a number of common properties. For example, they are more explicit in meaning, grammatical structure and logical relationship. Translators may try various means to render them simpler and easier to read. Given TL unique item under-representation and SL interference, translations from the same or similar source languages may share even more common features, which make translated texts more similar to each other than to native texts in the target language, thus lending evidence to the levelling out hypothesis. Nevertheless, levelling out or convergence depends to a great extent on the typological distance between the languages involved in translation, and it can involve many linguistic features of different kinds at various levels, which makes it diffi cult to quantify the empirical evidence to support this TUs hypothesis. 9.2 Contributions of the Research Presented in the Book 173

9.2 Contributions of the Research Presented in the Book

Since the 1950s, descriptive translation studies as proposed by Toury (1995 ) has attracted an ever growing interest to translation studies. This interest, in the form of corpus-based translation studies , has entered a booming period of development with the maturation of corpus methodologies since the 1990s. As we have seen in this book, research in CTS has been predominantly product oriented, i.e. comparing the linguistic and textual features of the translated language with those of the non- translated or native language. The common ground of these studies is the parallel and comparable corpora of translated and non-translated texts, which were built with similar sampling method and corpus design. But, as noted in previous chapters, most of the current studies on translational language and on translation universals have been concerned with only a small number of closely related European languages and the translation from these languages into English (cf. Mauranen and Kujamäki 2004 ). The only large-scale corpus in existence and used by most researchers is the Translational English Corpus (TEC) built by Mona Baker and her colleagues at Manchester University. Given that the research of translational English in relation to a few of its closely related European languages has indeed yielded some interesting fi ndings, it seems still questionable whether these linguistic features of translational English (and a couple of other European languages, e.g. German, Norwegian, etc.) can be generalised as far as to be “universal” in the translation of most languages. Apparently, the studies of the translations in typologically distant languages are expected to be more convincing than the studies based on only closely related languages. Although there are already some empirical studies on translational Chinese (as reviewed in Chap. 3 ), most of them are based on particular genres (e.g. translated novels or popular science). The linguistic variation across genres and registers is beyond most of these studies. However, as the studies of linguistic variation show (e.g. Biber 1988 , 1995 ; Xiao 2009a ), different genres or registers tend to have different linguistic preferences, especially in quantitative terms. If these variation factors are not considered, the so-called universals studies will not be possible to extend to the language as a whole, not to mention the translation in other languages. As such, we would be delighted to fi nd our fi ndings based on translational Chinese, one of the major languages in the world, genetically distant from English and other European languages, are trustworthy breakthrough to descriptive translation studies and particularly to the research of translation universals . In terms of methodology, researchers in recent translation studies tend to either focus on monolingual comparison of native texts and translated texts in the same target language (e.g. studies on translation universals carried out by Baker, Kenny, Laviosa and others, reviewed in detail in Chap. 3 ) or focus on bilingual contrast of source texts in relation to target texts (the latter is the most widely taken methodology so far). Different from these single-method studies, our research has taken the composite research method as proposed by McEnery and Xiao (2002 ), i.e. a com- binatory approach of both comparable and parallel corpora . This approach has 174 9 Conclusive Remarks enabled us to investigate translated texts from two different perspectives and to reach at a more systematic description of the multifacet characteristics of translational Chinese. We hope the readers will recognise the theoretical as well as paradigmatic value of the research presented in this book. As the research has demonstrated in great detail, we have made efforts to create a new composite approach based on balanced comparable and parallel corpora to translational varieties and translation universals in two genetically distant languages, English and Chinese. Hopefully, these results are not only a contribution to descriptive translation studies in general, but also abound in their practical applications, for example, the awareness of the qualities of the “ Third Code ” will help the translator to improve his or her actual translation. It is worthy of mentioning those contributions clearly in a short summary: fi rst of all, we made great efforts in collecting and compiling the balanced corpus of Chinese translations, i.e. the Zhejiang University Corpus of Translational Chinese ( ZCTC ), the fi rst corpus of translational Chinese in its type, comparable to its native and translational counterparts in both Chinese (LCMC) and English ( FLOB and Frown ). ZCTC is expected and has already been used in this book as an important database for a comprehensive research into translational Chinese, qualitatively as well as quantitatively. This research will be helpful in overcoming the bottleneck problem of previous TUs research which is by and large constrained among a small number of closely related European languages (mostly related to English). Second, this book is methodologically innovative in bringing together different approaches in contrastive linguistic studies and translation studies, particularly in providing a composite approach that involves both parallel and comparable corpora in the same research, instead of treating the two types of corpora separately. One of the advantages of this composite approach is that we are not only able to fi nd out the distinct features of translated Chinese texts, but also able to detect the level of source language (English) interference in Chinese translation. The third contribution of this book is a detailed and comprehensive description of the distinct lexical and grammatical features of translational Chinese, which will compensate the limitation of the current studies on translational language to a few closely related languages and a small number of macro-statistic linguistic features. We hope these objective descriptions of translational Chinese as one of the major languages will prove to be a landmark in descriptive translation studies.

9.3 Directions for Future Research

It is delightful to see that corpus-based translation studies , especially the research on the features of translational language, has become a new but thriving research hotspot in recent years (e.g. Wang 2011 ; Hu 2011 ). Along with the theoretical and methodological interest and a great number of case studies having been carried out or in progress, there are other interesting topics and promising directions for future research. 9.3 Directions for Future Research 175

First of all, the parallel and comparable corpora used in this research and others are all static corpora, which means they are at best only representative to a “snapshot” of English/Chinese written texts during the early 1990s. But if a large-scale English-Chinese bidirectional parallel corpus, which contains representative parallel texts between the two languages at different periods of time, for instance, from the beginning of the twentieth century when modern Chinese was still in its rudimentary state to the end of the twentieth century, it will be an ideal basis not only for synchronic contrastive research between English and Chinese but also an incubator for diachronic studies of the development of modern Chinese. Researchers will also be enabled to probe into such illuminating subjects as language contact between Chinese and English and the effect of English upon modern Chinese through translation. Fortunately, some Chinese researchers have already noticed the importance of such a corpus, who helped to launch a couple of bigger research projects involving a lot of researchers active in this area. Secondly, some features of translational language uncovered by our research can be found in other non-native linguistic variations (e.g. the learner’s language), which leads to a reasonable speculation that translation universals might also be mediation universals in similar mediated discourse, e.g. translation, learner’s language, non-native utterances by professionals, etc. This subject, though obviously beyond the scope of the current research, is worthy of further studies, because it will be valuable for translation training, foreign language teaching and professional communication. Last but not least, although the detailed studies in this book, which are based on our balanced corpora of native and translated Chinese texts, have revealed partial evidence for some of the translation universals hypotheses that were originally testifi ed on the basis of a small number of closely related western languages, predominantly English, we become suspicious of the use of translated and native English texts used in those studies, despite of the fact that the study of those texts is the origin of the TUs research. We perceive as a serious problem the incomparability or partial comparability of translated and native texts used by those studies; for example, the Translational English Corpus taken as representative of translated English texts is not comparable to the representative British National Corpus (BNC) of native English in terms of their sampling scales and methods. As reviewed in this book, the majority of texts in TEC are translated novels, which explains why the research of translational English has been mostly constrained to the register of fi ction. However, as we have shown, enormous variations of language use exist across registers. Any discussion of translation universals cannot be verifi ed by focusing on only one register or based on loosely comparable texts. As such, in order to solve those problems concerning the balance and comparability of corpora, a more reliable way is, as exemplifi ed by this study, to follow the blueprints of the classic native English corpora already in existence (e.g. the Freiburg-LOB Corpus of British English and the Frown Corpus of American English ) by strictly applying the same sampling standards and compiling methods of those corpora. We believe this might be the only way to remedy the above 176 9 Conclusive Remarks methodological defects in corpus-based translation studies. Only through the use of exactly comparable corpora can we really understand the typical features of translational language, including English, and, more importantly, the variation of these features across different registers. Apparently this is another topic which falls beyond the scope of this book. Appendices

Appendix 1: Tagset of LCMC and ZCTC

Appendix 1 is the tagset used by the Lancaster Corpus Mandarin Chinese (LCMC 2.0) and Zhejiang University Corpus of Translational Chinese (ZCTC). Both corpora are annotated with ICTCLAC 2008 developed by Chinese Academy of Sciences and processed to correct apparent systematic annotation errors. The following is a revised version based on the Chinese Part-of-Speech Tagset (version 3.0) developed by the Institute of Computing Technology, Chinese Academy of Science.

Tag Chinese Romanisation English a 形容词 xing rong ci adjective ad 副形词 fu xing ci adjective as adverbial ag 形容词性语素 xing rong ci xing yu su adjectival morpheme al 形容词性惯用语 xing rong ci xing guan adjectival formulaic expression yong yu an 名形词 ming xing ci adjective with nominal function b 区别词 qu bie ci modifi er (non-predicate noun modifi er) bg 区别词语素 qu bie ci yu su noun modifi er morpheme bl 区别词性惯用语 qu bie ci xing guan yong noun modifi er formulaic expression yu c 连词 lian ci conjunction cc 并列连词 bing lie lian ci coordinating conjunction cg 连词语素 lian ci yu su conjunction morpheme d 副词 fu ci adverb dg 副词性语素 fu ci xing yu su adverbial morpheme dl 副词性惯用语 fu ci xingguan yong yu adverbial formulaic expression (continued)

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 177 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6 178 Appendices

Tag Chinese Romanisation English e 叹词 tan ci interjection ew 句末标点 ju mo biao dian sentential punctuation (full stop, semi-colon, question mark, exclamation mark) f 方位词 fang wei ci space word fg 方位词语素 fang wei ci yu su space word morpheme g 语素 yu su morpheme h 前缀 qian zhui prefi x i 成语 cheng yu idiom k 后缀 hou zhui suffi x m 数词 shu ci numeral and quantifi er mg 数词性语素 shu ci xing yu su numeral and quantifi er morpheme mq 数量词 shu liang ci numeral-classifi er j 缩写 suo xie abbreviation l 惯用语 guan yong yu fi xed expressions n 名词 ming ci noun ng 名词性语素 ming ci xing yu su nominal morpheme nl 名词性惯用语 ming ci xingguan yong nominal formulaic expression yu nr 汉语人名 han yu ren ming person name nr1 汉语姓氏 han yu xing shi Chinese surname nr2 汉语名字 han yu ming zi Chinese fi rst name nrf 音译人名 yin yi ren ming transliterated foreign person name nrj 日语人名 ri yu ren ming Japanese name ns 地名 di ming place name nsf 音译地名 yin yi di ming transliterated foreign place name nt 机构团体名 ji gou tuan ti ming organization name nx 名词性字符串 ming ci xing zi fu chuan nominal character string nz 其它专名 qi ta zhuan ming other proper noun o 拟声词 ni sheng ci onomatopoeia p 介词 jie ci preposition pba 介词“把” jie ci“ba” preposition ba 把 pbei 介词 “被” jie ci“bei” preposition bei 被 pg 介词性语素 jie ci xing yu su preposition morpheme q 量词 liang ci classifi er qg 量词性语素 liang ci xing yu su classifi er morpheme qt 时量词 shi liang ci temporal classifi er qv 动量词 dong liang ci verbal classifi er r 代词 dai ci pronoun rg 代词性语素 dai ci xing yu su pronominal morpheme rr 人称代词 ren cheng dai ci personal pronoun ry 疑问代词 yiwen dai ci interrogative pronoun rys 处所疑问代词 chu suo yi wen dai ci place interrogative pronoun ryt 时间疑问代词 shi jian yi wen dai ci temporal interrogative pronoun (continued) Appendices 179

Tag Chinese Romanisation English ryv 谓词性疑问代词 wei ci xingyiwen dai ci verbal interrogative pronoun rz 指示代词 zhi shi dai ci deictic pronoun rzs 处所指示代词 chu suo zhi shi dai ci place pronoun rzt 时间指示代词 shi jian zhi shi dai ci temporal pronoun rzv 谓词性指示代词 wei ci xing zhi shi dai ci verbal pronoun s 处所词 chu suo ci place word t 时间词 shi jian ci time word tg 时间词性语素 shi jian ci xing yu su time word morpheme u auxiliary zhu ci auxiliary ude1 的 , 底 de, di auxiliary de 的, di 底 ude2 地 di auxiliary di 地 ude3 得 de auxiliary de 得 u deng 等 , 等等, 云云 deng, dengdeng, yunyun auxiliary deng 等, dengdeng 等等, yunyun 云云 udh …的话 …de hua auxiliary …dehua …的话 uguo 过 guo auxiliary guo 过 ule 了 le auxiliary le 了 ulian 连 lian auxiliary lian 连 uls 来讲, 来说, 而言, lai jiang, lai shuo, er auxiliary laijiang 来讲, laishuo 来说来 yan, shuo lai 说, eryan 而言, shuolai 说来 usuo 所 suo auxiliary suo 所 uyy 一样, 一般, 似的, yi yang, yi ban, si de, auxiliary yiyang 一样, yiban 一般, 般 ban side 似的, ban 般 uzhe 着 zhe auxiliary zhe 着 uzhi 之 zhi auxiliary zhi 之 v 动词 dong ci verb vd 副动词 fu dong ci adverbial use of verb vf 趋向动词 qu xiang dong ci directional verb vg 动词性语素 dong ci xing yu su verb morpheme vi 不及物动词 bu ji wu dong ci intransitive verb vl 动词性惯用语 dong ci xing guan yong verbal formulaic expression yu vn 名动词 ming dong ci nominal use of verb vshi 动词 “是” dong ci“shi” verb shi 是 vx 形式动词 xing shi dong ci pro-verb vyou 动词 “有” dong ci“you” verb you 有 w 标点符号 biao dian fu hao symbol and punctuation wb 百分号或千分号 bai fen hao huo qian fen percentage and permillage sign hao wd 逗号 dou hao full or half-length comma “,” “,” wj 全角句号 quan jiao ju hao full stop of full length “。” wkz 左括号 zuo kuo hao opening brackets wky 右括号 you kuo hao closing brackets wm 冒号 mao hao colon (continued) 180 Appendices

Tag Chinese Romanisation English wn 顿号 dun hao full-length enumeration mark , wp 破折号 po zhe hao dash ws 省略号 sheng lue hao full-length ellipsis wy 引号 yin hao quote x 字符串 zi fu chuan non-word character string y 语气词 yu qi ci particle yg 语气词语素 yu qi ci yu su particle morpheme z 状态词 zhuang tai ci descriptive word

Appendix 2: Keywords in ZCTC

The top 100 keywords in the Zhejiang University Corpus of Translational Chinese (ZCTC) sorted according to the “keyness” of the words. The reference corpus used for this list is the Lancaster Corpus of Mandarin Chinese (LCMC).

Keyword Romanisation English gloss 1 公司 gongsi company 2 美国 Meiguo USA 3 的 de de 4 A A A 5 E E E 6 他 ta he 7 我 wo I 8 N N N 9 O O O 10 C C C 11 I I I 12 S S S 13 T T T 14 会 hui will, shall 15 他们 tamen they 16 美元 meiyuan dollar 17 她 ta she, her 18 R R R 19 英国 yingguo UK 20 L L L 21 这些 zhexie these 22 可能 keneng maybe 23 B B B 24 该 gai should (continued) Appendices 181

Keyword Romanisation English gloss 25 M M M 26 它 ta it 27 D D D 28 在 zai at, in, on 29 将 jiang will 30 P P P 31 但是 danshi but 32 如果 ruguo if 33 # # # 34 所有 suoyou all 35 你 ni you 36 全球 quanqiu global 37 网络 wangluo internet 38 斯 si si 39 社论 shelun editorial 40 克林顿 kelindun Clinton 41 俄罗斯 eluosi Russia 42 预计 yuji estimate 43 因为 yinwei because 44 第 di No. 45 关税 guanshui customs 46 那些 naxie those 47 H H H 48 提供 tigong supply 49 澳大利亚 aodaliya Australia 50 资本 ziben capital 51 任何 renhe any 52 银行 yinxing bank 53 风险 fengxian risk 54 品牌 pinpai brand 55 贸易 maoyi commerce 56 U U U 57 亚洲 yazhou Asia 58 款 kuan section 59 约翰 yuehan John 60 最 zui superlative 61 我们 women we 62 纽约 niuyue New York 63 V V V 64 它们 tamen they 65 或 huo or 66 题 ti issue, item 67 政府 zhengfu government 68 所 suo suo (continued) 182 Appendices

Keyword Romanisation English gloss 69 其他 qita other 70 产量 chanliang production 71 F F F 72 因特网 yintewang Internet 73 这种 zhezhong this type 74 模式 moshi model 75 可以 keyi may 76 耶稣 yesu Jesus 77 药物 yaowu medicine 78 税率 shuilu tax rate 79 东盟 dongmeng ASEAN 80 货物 huowu products 81 亿 yi thousand million 82 金融 jinrong ﬁ nance 83 华盛顿 huashengdun Washington 84 治疗 zhiliao treatment 85 市场 shichang market 86 已经 yijing already 87 上月 shangyue last month 88 成本 chengben cost 89 也许 yexu probably 90 进口 jinkou import 91 网站 wangzhan website 92 纳米 nami Nanotech 93 钻石 zuanshi diamond 94 像 xiang as if 95 补贴 butie subsidy 96 大多数 daduoshu most, majority 97 危机 weiji crisis 98 大学 daxue university 99 适用 shiyong applicable 100 汤姆 tangmu Tom Appendices 183

Appendix 3: Notes for the Corpora and Tools Available with This Book

The Lancaster Corpus of Mandarin Chinese (LCMC, version 2.0) and the Zhejiang University Corpus of Translational Chinese (ZCTC) together with Xaira, the corpus concordancing and analysing tool, are made publicly available for the readers of this book. The two corpora are already indexed with Xiara indexer and can be used directly on the reader’s computer. Due to the incomparability of different versions of Xaira, readers who have purchased the XML CD disc of the British National Corpus (BNC) will need Xaira (version 1.23) to search the corpus. For these readers, we also prepare indices with version 1.23. The readers can download and install the respective Xaira program packages. The two corpora, LCMC and ZCTC, will be available for use by just opening the Xaira Client. Alternatively, the readers can open the root folder of the corpora, and double click on the ﬁ le named “xcorpus ” to run the Client program for the corpora. The Unicode versions of the two corpora ready for use with WordSmith Tools (version 4.0 or 5.0) are also available to download. Please visit the following website to download the associated corpora and corpus tools: www.lancs.ac.uk/fass/projects/corpus/CBSTC/. The password for decompacting all ﬁ les is “ jiaodachuban ”. References

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A E Aspect marker , 7, 48, 90, 149–151, 160, 166 Existential you sentence , 135–138, 165 Explicitation , 16–18, 20, 22, 23, 26–29, 34, 75, 101, 103–105, 112, 119, 159, 163, 169 B ba construction , 32, 131–135, 164 bei passive , 33, 121, 124, 127–130, 163, F 164, 171, 172 Freiburg-LOB corpus of British English British National Corpus (BNC) , 13, 16, 41, (FLOB), 12, 40–43, 49, 53, 55, 58, 54, 63, 67, 108, 112, 175 63, 77, 79–81, 83, 116, 123, 124, Brown corpus , 12, 32, 40, 41 160, 174, 175 Frown , 12, 41, 42, 49, 174, 175 Function word , 27–29, 70, 89, 90, 93, 95, C 96, 98, 99, 152, 153, 157, 158, 160, Classiﬁ er , 93, 146, 147, 149, 166 161, 166, 170, 171 Comparable corpus , 11–13, 17, 112, 157 Conjunction , 18, 23, 28, 29, 92, 104–106, 162, 170 G Connective , 17, 18, 26, 104, 112, 114, 161 General Chinese-English Parallel Corpus Content word , 30, 68, 89, 90, 93, 96, 161, 170 (GCEPC), 29–31, 34, 53, 54, 116, Convergence , 20, 22, 25, 86, 160, 162, 169, 172 129, 132, 153 Corpus-based translation studies (CTS) , Grammatical explicitation , 28, 157, 158, 2, 4–6, 9, 11, 13, 21, 26, 27, 33, 162, 170 34, 173, 174 Grammatical simpliﬁ cation , 170 Corpus linguistics , 3, 4, 9, 10, 21, 38, 41, 65, 68, 76 CTS. See Corpus-based translation H studies (CTS) High frequency word , 7, 30, 67, 71–73, 89, 158, 159, 161, 162

D Demonstrative pronoun , 90, 101, 103, 161 I Descriptive translation studies (DTS) , 2–4, 6, Idiom , 107–110, 163, 172 16, 20–22, 173, 174 Imperfective aspect marker , 90, 149–151, 166 Discourse marker , 25, 110, 111 Interrogative pronoun , 101, 103, 161, 167

© Shanghai Jiao Tong University Press and Springer-Verlag Berlin Heidelberg 2015 203 R. Xiao, X. Hu, Corpus-Based Studies of Translational Chinese in English- Chinese Translation, New Frontiers in Translation Studies, DOI 10.1007/978-3-642-41363-6 204 Index

K N Keyword , 79, 89–91, 93, 160, 161 Negative keyword/class. See Keyword analysis , 58, 65, 74, 89 Non-sentence-ﬁ nal punctuation class , 89, 91, 93, 98, 137, 142, 160 mark , 99, 101 cluster , 79, 95, 98, 99, 162 Normalisation , 16, 18, 20, 22, 24, 25, 33, 83, negative keyword , 74, 89, 90, 160 101, 106, 108, 109, 116, 121, 127, negative keyword-class , 91, 92, 98, 160–163, 172 137, 162 Notional passive , 122, 126 translated and non-translated corpora , 30

O L Oral reformulation marker , 114–117, 163 Lancaster Corpus of Mandarin Chinese (LCMC) , 30, 40, 49, 68, 69, 71, 72, 74, 75, 77, 79–81, 83, 91, 93, 95, 96, P 99, 104, 109, 114, 123, 124, 137, 139, Parallel corpus , 2, 6, 11, 12, 14, 15, 17, 28, 29, 143, 145, 150 31–33, 54, 112, 116, 117, 127, 129, Lancaster/Oslo-Bergen corpus of British 150, 164, 165, 173, 175 English (LOB), 12, 41 Perfective aspect marker , 90, 149, 150, 166 LCMC. See Lancaster Corpus of Mandarin Personal pronoun , 26, 29, 30, 90, Chinese (LCMC) 101, 103, 161 LD. See Lexical density (LD) Positive keyword. See keyword Levelling out , 22, 25, 86, 160, 162, 172 . Pronoun , 90, 91, 101, 103, 136, 161 See Convergence Punctuation mark , 99–101, 159, 162, Lexical density (LD) , 7, 26, 30, 67–69, 170, 172 71, 92, 157, 158 Lexical simplifi cation , 30, 158, 161, 170 Lexical variability , 68, 71, 72, 158 R Lian construction , 144, 145 Reformulation marker , 110, 112–119, 163 Light verb , 90, 92, 172 Literate reformulation marker , 114–117 LL. See Log-likelihood test (LL) S LOB. See Lancaster/Oslo-Bergen corpus Sanitisation , 16, 17, 20, 22, 24, 27 of British English (LOB) Semantic explicitation , 101, 161, 163, Logical explicitation , 170 169, 170 Log-likelihood test (LL), 63, 79, 93, 104, 109, Semantic simplifi cation , 157, 161, 170 114, 123, 124, 133, 137, 139, 141, 143, Sentence-fi nal punctuation mark , 99, 100, 172 145, 146, 150, 151 Shi sentence , 138–142, 165 Long passive , 33, 123, 164 Short passive , 123, 164 Low frequency word , 7, 67, 72, 73, 89, 105, Simplifi cation , 16, 17, 20, 22–24, 27, 29, 30, 158, 160 34, 73, 105, 106, 116, 135, 138, 153, 158–160, 162, 165, 170–172 Source language (SL) , 1, 5, 27, 53, 159 M Source language interference , 2, 22, 31, 33, Mean 123, 126–131, 139, 144, 154, 159–162, paragraph length , 77, 158, 159 164, 165, 167, 171, 172 sentence length , 23, 75, 77, 158, 159, 170 Source language shining through , 22, 25, 121, word length , 72, 73, 158, 170, 172 126, 161, 169 Mean sentence segment length (MSSL) , Standardised type token ratio (STTR) , 75–77, 158, 159, 170, 171 30, 68–72, 158 Modal particle , 93, 154, 161, 162, 167, 171 Structural auxiliary , 7, 33, 74, 138, MSSL. See Mean sentence segment length 141, 142, 152, 153, 160, 161, (MSSL) 166, 167, 171 Index 205

STTR. See Standardised type token Translation universals hypotheses , 7, 25, 26, ratio (STTR) 34, 35, 157, 169, 175. S-universals , 20, 22, 27 See also Translation universals (TUs) Suo sentence , 142–144, 165, 166 TTR. See Type token ratio (TTR) T-universals , 20, 22, 23, 27, 29 TUs. See Translation universals (TUs) T Type token ratio (TTR) , 18, 19, 26, 30, 68 Target language (TL) , 1, 5, 11, 14–22, 24, 25, 27, 138, 159, 163, 167, 171–173 U under-representation , 22, 25, 138, Under-representation. See Target language 145, 149, 154, 162, 163, 165–167, (TL), under-representation 169, 171, 172 TEC. See Translational English Corpus (TEC) W Textual information load , 7, 67, 71, 157, Word-class , 162 161, 162 Word-class cluster , 161, 162 Third Code , 1, 2, 20, 21, 27, 169, Word cluster , 17, 24, 58, 59, 63, 67, 78–81, 172, 174 83, 86, 159, 160, 162, 163, 170, 172 TL. See Target language (TL) Translational English Corpus (TEC) , 5, 16, 173, 175 Z Translationese , 3, 20, 21, 43, 141, 171 Zhejiang University Corpus of Translational Translation universals (TUs) , 1, 2, 4–7, 16, Chinese (ZCTC), 37, 40, 48, 68, 69, 71, 20–22, 27, 34, 35, 75, 155, 167, 169, 72, 74, 75, 77, 79–81, 83, 123, 124, 173, 174, 175 137, 139, 143, 145, 150, 174