DOCSLIB.ORG

Automatic Interpretation System Integrating Free-Style Sentence Translation and Parallel Text Based Translation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatic Interpretation System Integrating Free-Style Sentence Translation and Parallel Text Based Translation Proceedings of the Workshop on Speech-to-Speech Translation: Algorithms and Systems, Philadelphia, July 2002, pp. 85-92. Association for Computational Linguistics. Automatic Interpretation System Integrating Free-style Sentence Translation and Parallel Text Based Translation Takahiro Ikeda Shinichi Ando Kenji Satoh Akitoshi Okumura Takao Watanabe Multimedia Res. Labs. NEC Labs. 4-1-1 Miyazaki, Miyamae-ku, Kawasaki, Kanagawa 216 [email protected], [email protected], [email protected], [email protected], [email protected] Abstract being produced by the total system even for a lim- ited domain. Clearly, we need ways to complement This paper proposes an automatic in- speech-to-speech translation systems that cannot re- terpretation system that integrates free- liably produce a correct result. style sentence translation and parallel text Although some robust methods that make the er- based translation. Free-style sentence roneous results of other components acceptable have translation accepts natural language sen- been proposed (Yumi et al., 1997; Furuse et al., tences and translates them by machine 1998), there is no guarantee that the final output translation. Parallel text based translation from a system will be appropriate even with these provides a proper translation for a sen- methods. To deal with this problem, we have taken a tence in the parallel text by referring to a more practical approach to developing an automatic corresponding translation of the sentence interpretation system where the user can obtain a and supplements free-style sentence trans- correct result instead of having to apply additional lation. We developed a prototype of an au- operations and judgment. tomatic interpretation system for Japanese In actual use of a speech-to-speech translation overseas travelers with parallel text based system, an error in the speech-recognition or speech- translation using 9206 parallel bilingual synthesis components is not a large problem if the sentences prepared in task-oriented man- system has a screen that displays each result. The ner. Evaluation results show that the par- user of the system can correct errors in the recogni- allel text based translation covers 72% of tion result on the screen, and can communicate by typical utterances for overseas travel and showing the other person the translated sentence on the user can easily find an appropriate sen- the screen. tence from a natural utterance for 64% of On the other hand, an error in the machine- typical traveler’s tasks. This indicates that translation component is critical because a user who the user can benefit from reliable transla- is not familiar with the target language is unlikely tion based on parallel text for fundamental to notice the error in some cases. When a nonsensi- utterances necessary for overseas travel. cal sentence is generated by machine translation, the user may realize that the listener does not understand 1 Introduction the translated sentence. However, when a plausible sentence that means something different from the in- A speech-to-speech translation system must inte- tended meaning is generated by the machine trans- grate at least three components — speech recogni- lation, the user may incorrectly assume that the ut- tion, machine translation, and speech synthesis. In terance was properly communicated. Consequently, practice, each component does not always output the user can seldom be sure that the listener cor- the correct result for various inputs, and an error rectly understood the intended meaning when using in one component often leads to an incorrect result a speech-to-speech translation system. A conversa- tion could continue for some time before it became 2 The Integration Model apparent that the two sides misunderstood what the 2.1 User Interface other was saying. Moreover, if the user realizes that there is an er- Although parallel text based translation provides a ror in the machine translation, correcting it will be correct result, the registered parallel bilingual sen- difficult. Without knowing the source of the error, tences cannot cover all possible utterances by the the user cannot modify the input to obtain a correct user in the target domain. Free-style sentence trans- result. lation, on the contrary, accepts free-style input sen- These error problems severely limit the usability tences but provides no guarantee as to the quality of of speech-to-speech translation. results. In this paper, we propose an automatic interpreta- For many routine situations, users will clearly tion system that integrates free-style sentence trans- benefit from using parallel text based translation. lation and parallel text based translation. In this sys- In such cases, the system will probably include a tem, free-style sentence translation accepts natural sentence that totally or partially fits what they want language sentences and translates them by machine to say. To ensure high translation reliability, users translation without guaranteeing the quality of the should use free-style sentence translation only for translation. On the other hand, parallel text based utterances not covered by the registered sentences. translation uses parallel bilingual sentences regis- However, users usually will not know what sen- tered in the system and translates a registered sen- tences are registered in the system and will have to tence by referring to the corresponding translation. search for an appropriate sentence before they can Although this translation process limits the input to use parallel text based translation. In some cases, the registered sentences, it is a robust means of han- user will be forced to use free-style sentence trans- dling input with recognition errors and consistently lation if unable to find an appropriate sentence. provides a correct translation. We integrated these A seamless user interface that allows the user to two types of translation to realize a robust transla- easily switch between free-style sentence transla- tion system where the two types of translation com- tion and parallel text based translation is therefore pensate for the shortcomings of each other. needed in a system integrating these two forms of For appropriate integration of free-style sentence translation. Two conditions in particular had to be translation and parallel text based translation, we met to make the system easy to use. had to consider three main points. 1. The user should be able to use an input sen- tence seamlessly as both a source sentence for 1. User interface: how best to present the two free-style sentence translation and a key sen- functions to the user? tence for registered sentence retrieval. 2. Content of registered sentences: How many ut- 2. The user should be able to use each sentence in- terances should be covered by registered sen- cluded in the results of the registered sentence tences? retrieval and the input sentence as a source sentence for translation. (The former would 3. Retrieval system: What methods of searching be used for parallel text based translation, and among the registered sentences should be pro- the latter would be used for free-style sentence vided to the user? translation.) In this paper, we discuss these three points with 2.2 Content of Registered Sentences respect to a translation system for Japanese travelers Registered sentences must cover the utterances nec- in the overseas travel domain. We construct a model essary for accomplishing typical tasks in the target of the integration of free-style sentence translation domain to provide correct translation for minimal and parallel text based translation in Section 2. We communication. In a translation system for overseas describe a prototype system based on the model in travelers, some typical tasks are changing money, Section 3 and evaluate it in Section 4. Related work checking in at a hotel, and ordering at a restaurant. on translation systems utilizing parallel text are dis- We adopted a three-tier model that consists of cussed in Section 5, and we conclude in Section 6. scenes, tasks, and subtasks to prepare a sufficient set Table 1: Examples of scenes, tasks, subtasks, and templates of sentences Scene Task Subtask Template of sentence Hotel Check-in Checking in I’d like to check in, please. Hotel Check-in Requesting a type of room I’d like a room with the ocean view. Restaurant Order Requesting cooking time for your steak Medium, please. Restaurant Order Asking what they recommend What do you recommend for appetizers? of necessary sentences to be registered in the sys- for the respondent to choose from. The system keeps tem. A scene comprised a place or situation that typical responses in parallel bilingual form for each corresponds to where a traveler is likely to be (e.g., a registered sentence that the traveler can use and dis- hotel) and a problem that could arise. We made a list plays these as candidate responses when the traveler of typical travelers’ tasks that would be necessary in uses the sentence. The system then shows the trav- various travel scenes, divided each task into smaller eler the translation of the response selected by the primitive tasks (subtasks), and assigned a sentence respondent. template to each subtask based on the model. This approach enables travelers to obtain a reli- In general, more than one round of conversation able response and also enables respondents to easily is necessary to accomplish each task. We assumed select an appropriate response. that a task would consist of smaller subtasks, each of which would correspond to one round of conver- 2.3 Retrieval System sation that consisted simply of an utterance from a The retrieval system to search for a registered sen- traveler to a respondent and a response from the re- tence that we use is based on a combination of three spondent to the traveler. For example, the task of conditions — the natural language sentence, scene, checking in to a hotel consists of subtasks such as and action.
Load more

Recommended publications
  • Champollion: a Robust Parallel Text Sentence Aligner
    Champollion: A Robust Parallel Text Sentence Aligner Xiaoyi Ma Linguistic Data Consortium 3600 Market St. Suite 810 Philadelphia, PA 19104 [email protected] Abstract This paper describes Champollion, a lexicon-based sentence aligner designed for robust alignment of potential noisy parallel text. Champollion increases the robustness of the alignment by assigning greater weights to less frequent translated words. Experiments on a manually aligned Chinese – English parallel corpus show that Champollion achieves high precision and recall on noisy data. Champollion can be easily ported to new language pairs. It’s freely available to the public. framework to find the maximum likelihood alignment of 1. Introduction sentences. Parallel text is a very valuable resource for a number The length based approach works remarkably well on of natural language processing tasks, including machine language pairs with high length correlation, such as translation (Brown et al. 1993; Vogel and Tribble 2002; French and English. Its performance degrades quickly, Yamada and Knight 2001;), cross language information however, when the length correlation breaks down, such retrieval, and word disambiguation. as in the case of Chinese and English. Parallel text provides the maximum utility when it is Even with language pairs with high length correlation, sentence aligned. The sentence alignment process maps the Gale-Church algorithm may fail at regions that contain sentences in the source text to their translation. The labor many sentences with similar length. A number of intensive and time consuming nature of manual sentence algorithms, such as (Wu 1994), try to overcome the alignment makes large parallel text corpus development weaknesses of length based approaches by utilizing lexical difficult.
    [Show full text]
  • The Web As a Parallel Corpus
    The Web as a Parallel Corpus Philip Resnik∗ Noah A. Smith† University of Maryland Johns Hopkins University Parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the STRAND system for mining parallel text on the World Wide Web, first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new content- based measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale. Finally, the value of these techniques is demonstrated in the construction of a significant parallel corpus for a low-density language pair. 1. Introduction Parallel corpora—bodies of text in parallel translation, also known as bitexts—have taken on an important role in machine translation and multilingual natural language processing. They represent resources for automatic lexical acquisition (e.g., Gale and Church 1991; Melamed 1997), they provide indispensable training data for statistical translation models (e.g., Brown et al. 1990; Melamed 2000; Och and Ney 2002), and they can provide the connection between vocabularies in cross-language information retrieval (e.g., Davis and Dunning 1995; Landauer and Littman 1990; see also Oard 1997). More recently, researchers at Johns Hopkins University and the
    [Show full text]
  • Resourcing Machine Translation with Parallel Treebanks John Tinsley
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DCU Online Research Access Service Resourcing Machine Translation with Parallel Treebanks John Tinsley A dissertation submitted in fulfilment of the requirements for the award of Doctor of Philosophy (Ph.D.) to the Dublin City University School of Computing Supervisor: Prof. Andy Way December 2009 I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of Ph.D. is entirely my own work, that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge breach any law of copyright, and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work. Signed: (Candidate) ID No.: Date: Contents Abstract vii Acknowledgements viii List of Figures ix List of Tables x 1 Introduction 1 2 Background and the Current State-of-the-Art 7 2.1 ParallelTreebanks ............................ 7 2.1.1 Sub-sentential Alignment . 9 2.1.2 Automatic Approaches to Tree Alignment . 12 2.2 Phrase-Based Statistical Machine Translation . ...... 14 2.2.1 WordAlignment ......................... 17 2.2.2 Phrase Extraction and Translation Models . 18 2.2.3 ScoringandtheLog-LinearModel . 22 2.2.4 LanguageModelling . 25 2.2.5 Decoding ............................. 27 2.3 Syntax-Based Machine Translation . 29 2.3.1 StatisticalTransfer-BasedMT . 30 2.3.2 Data-OrientedTranslation . 33 2.3.3 OtherApproaches ........................ 35 2.4 MTEvaluation.............................
    [Show full text]
  • Towards Controlled Counterfactual Generation for Text
    The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Nishtha Madaan, Inkit Padhi, Naveen Panwar, Diptikalyan Saha 1IBM Research AI fnishthamadaan, naveen.panwar, [email protected], [email protected] Abstract diversity will ensure high coverage of the input space de- fined by the goal. In this paper, we aim to generate such Machine Learning has seen tremendous growth recently, counterfactual text samples which are also effective in find- which has led to a larger adoption of ML systems for ed- ing test-failures (i.e. label flips for NLP classifiers). ucational assessments, credit risk, healthcare, employment, criminal justice, to name a few. Trustworthiness of ML and Recent years have seen a tremendous increase in the work NLP systems is a crucial aspect and requires guarantee that on fairness testing (Ma, Wang, and Liu 2020; Holstein et al. the decisions they make are fair and robust. Aligned with 2019) which are capable of generating a large number of this, we propose a framework GYC, to generate a set of coun- test-cases that capture the model’s ability to misclassify or terfactual text samples, which are crucial for testing these remove unwanted bias around specific protected attributes ML systems. Our main contributions include a) We introduce (Huang et al. 2019), (Garg et al. 2019). This is not only lim- GYC, a framework to generate counterfactual samples such ited to fairness but the community has seen great interest that the generation is plausible, diverse, goal-oriented and ef- in building robust models susceptible to adversarial changes fective, b) We generate counterfactual samples, that can direct (Goodfellow, Shlens, and Szegedy 2014; Michel et al.
    [Show full text]
  • Natural Language Processing Security- and Defense-Related Lessons Learned
    July 2021 Perspective EXPERT INSIGHTS ON A TIMELY POLICY ISSUE PETER SCHIRMER, AMBER JAYCOCKS, SEAN MANN, WILLIAM MARCELLINO, LUKE J. MATTHEWS, JOHN DAVID PARSONS, DAVID SCHULKER Natural Language Processing Security- and Defense-Related Lessons Learned his Perspective offers a collection of lessons learned from RAND Corporation projects that employed natural language processing (NLP) tools and methods. It is written as a reference document for the practitioner Tand is not intended to be a primer on concepts, algorithms, or applications, nor does it purport to be a systematic inventory of all lessons relevant to NLP or data analytics. It is based on a convenience sample of NLP practitioners who spend or spent a majority of their time at RAND working on projects related to national defense, national intelligence, international security, or homeland security; thus, the lessons learned are drawn largely from projects in these areas. Although few of the lessons are applicable exclusively to the U.S. Department of Defense (DoD) and its NLP tasks, many may prove particularly salient for DoD, because its terminology is very domain-specific and full of jargon, much of its data are classified or sensitive, its computing environment is more restricted, and its information systems are gen- erally not designed to support large-scale analysis. This Perspective addresses each C O R P O R A T I O N of these issues and many more. The presentation prioritizes • identifying studies conducting health service readability over literary grace. research and primary care research that were sup- We use NLP as an umbrella term for the range of tools ported by federal agencies.
    [Show full text]
  • A User Interface-Level Integration Method for Multiple Automatic Speech Translation Systems
    A User Interface-Level Integration Method for Multiple Automatic Speech Translation Systems Seiya Osada1, Kiyoshi Yamabana1, Ken Hanazawa1, Akitoshi Okumura1 1 Media and Information Research Laboratories NEC Corporation 1753, Shimonumabe, Nakahara-Ku, Kawasaki, Kanagawa 211-8666, Japan {s-osada@cd, k-yamabana@ct, k-hanazawa@cq, a-okumura@bx}.jp.nec.com Abstract. We propose a new method to integrate multiple speech translation systems based on user interface-level integration. Users can select the result of free-sentence speech translation or that of registered sentence translation without being conscious of the configuration of the automatic speech translation system. We implemented this method on a portable device. Keywords: speech translation, user interface, machine translation, registered sentence retrieval, speech recognition 1 Introduction There have been many researches on speech-to-speech translation systems, such as NEC speech translation system[1], ATR-MATRIX[2] and Verbmobil[3]. These speech-to-speech translation systems include at least three components: speech recognition, machine translation, and speech synthesis. However, in practice, each component does not always output the correct result for various inputs. In actual use of a speech-to-speech translation system with a display, the speaker using the system can examine the result of speech recognition on the display. Accordingly, when the recognition result is inappropriate, the speaker can correct errors by speaking again to the system. Similarly, when the result of speech synthesis is not correct, the listener using the system can examine the source sentence of speech synthesis on the display. On the other hand, the feature of machine translation is different from that of speech recognition or speech synthesis, because neither the speaker nor the listener using the system can confirm the result of machine translation.
    [Show full text]
  • Lines: an English-Swedish Parallel Treebank
    LinES: An English-Swedish Parallel Treebank Lars Ahrenberg NLPLab, Human-Centered Systems Department of Computer and Information Science Link¨opings universitet [email protected] Abstract • We can investigate the distribution of differ- ent kinds of shifts in different sub-corpora and This paper presents an English-Swedish Par- characterize the translation strategy used in allel Treebank, LinES, that is currently un- terms of these distributions. der development. LinES is intended as a resource for the study of variation in trans- In this paper the focus is mainly on the second as- lation of common syntactic constructions pect, i.e., on identifying translation correspondences from English to Swedish. For this rea- of various kinds and presenting them to the user. son, annotation in LinES is syntactically ori- When two segments correspond under translation ented, multi-level, complete and manually but differ in structure or meaning, we talk of a trans- reviewed according to guidelines. Another lation shift (Catford, 1965). Translation shifts are aim of LinES is to support queries made in common in translation even for languages that are terms of types of translation shifts. closely related and may occur for various reasons. This paper has its focus on structural shifts, i.e., on 1 Introduction changes in syntactic properties and relations. Translation shifts have been studied mainly by The empirical turn in computational linguistics has translation scholars but is also of relevance to ma- spurred the development of ever new types of basic chine translation, as the occurrence of translation linguistic resources. Treebanks are now regarded as shifts is what makes translation difficult.
    [Show full text]
  • Cross-Lingual Bootstrapping of Semantic Lexicons: the Case of Framenet
    Cross-lingual Bootstrapping of Semantic Lexicons: The Case of FrameNet Sebastian Padó Mirella Lapata Computational Linguistics, Saarland University School of Informatics, University of Edinburgh P.O. Box 15 11 50, 66041 Saarbrücken, Germany 2 Buccleuch Place, Edinburgh EH8 9LW, UK [email protected] [email protected] Abstract Frame: COMMITMENT This paper considers the problem of unsupervised seman- tic lexicon acquisition. We introduce a fully automatic ap- SPEAKER Kim promised to be on time. proach which exploits parallel corpora, relies on shallow text ADDRESSEE Kim promised Pat to be on time. properties, and is relatively inexpensive. Given the English MESSAGE Kim promised Pat to be on time. FrameNet lexicon, our method exploits word alignments to TOPIC The government broke its promise generate frame candidate lists for new languages, which are about taxes. subsequently pruned automatically using a small set of lin- Frame Elements MEDIUM Kim promised in writing to sell Pat guistically motivated filters. Evaluation shows that our ap- the house. proach can produce high-precision multilingual FrameNet lexicons without recourse to bilingual dictionaries or deep consent.v, covenant.n, covenant.v, oath.n, vow.n, syntactic and semantic analysis. pledge.v, promise.n, promise.v, swear.v, threat.n, FEEs threaten.v, undertake.v, undertaking.n, volunteer.v Introduction Table 1: Example frame from the FrameNet database Shallow semantic parsing, the task of automatically identi- fying the semantic roles conveyed by sentential constituents, is an important step towards text understanding and can ul- instance, the SPEAKER is typically an NP, whereas the MES- timately benefit many natural language processing applica- SAGE is often expressed as a clausal complement (see the ex- tions ranging from information extraction (Surdeanu et al.
    [Show full text]
  • Parallel Texts
    Natural Language Engineering 11 (3): 239–246. c 2005 Cambridge University Press 239 doi:10.1017/S1351324905003827 Printed in the United Kingdom Parallel texts RADA MIHALCEA Department of Computer Science & Engineering, University of North Texas, P.O. Box 311366, Denton, TX 76203 USA e-mail: [email protected] MICHEL SIMARD Xerox Research Centre Europe, 6, Chemin de Maupertuis, 38240 Meylan, France e-mail: [email protected] (Received May 1 2004; revised November 30 2004 ) Abstract Parallel texts1 have become a vital element for natural language processing. We present a panorama of current research activities related to parallel texts, and offer some thoughts about the future of this rich field of investigation. 1 Introduction Parallel texts have become a vital element in many areas of natural language processing (NLP). They represent one of the richest and most versatile sources of knowledge for NLP, and have been used successfully not only in problems that are intrinsically multilingual, such as machine translation and cross-lingual information retrieval, but also as an indirect way of attacking “monolingual” problems, for example in semantic and syntactic analysis. Why have parallel texts proven such a fruitful resource? Most likely because of their ability to represent meaning: the translation of a text in another language can be seen as a semantic representation of that text, which opens the doors to a tremendously large number of language processing applications that operate on such representations. In his famous memorandum from 1949, Warren Weaver wrote: “When I look at an article in Russian, I say: ‘This is really written in English, but it has been coded in some strange symbols.
    [Show full text]
  • Open Architecture for Multilingual Parallel Texts M.T
    Open architecture for multilingual parallel texts M.T. Carrasco Benitez Luxembourg, 28 August 2008, version 1 1. Abstract Multilingual parallel texts (abbreviated to parallel texts) are linguistic versions of the same content (“translations”); e.g., the Maastricht Treaty in English and Spanish are parallel texts. This document is about creating an open architecture for the whole Authoring, Translation and Publishing Chain (ATP-chain) for the processing of parallel texts. 2. Summary 2.1. Next steps The next steps should be: • Administrative organisation: create a coordinating organisation and approach the existing organisations that might cooperate; e.g., IETF, LISA, OASIS, W3C. • Public discussion: with an emailing list (or similar) to reach a rough consensus, in particular on aspects such as the required specifications. Organise the necessary meeting(s). • Tools: implement some tools. This might be done simultaneously with the public discussion to better illustrate the approach and support the discussion. 2.2. Best approaches To obtain the best quality, speed and the lowest possible cost (QSC) in the production of parallel texts, one should aim for: • Generating all the linguistic versions ready for publication, from linguistic resources. Probably one of the best approaches. • Seamless ATP-chain implementations. • Authoring: Computer-aided authoring (CAA) tools with a controlled authoring environment; it should deliver source texts prepared for translation. • Translation: Computer-aided translation (CAT) tools to allow translators to focus only in translating and unburden translators from auxiliary tasks such as formatting. These tools should have functionalities such as side-by-side editor and the re-use of previous translations. • Publishing: Computer-aided publishing (CAP) tools to minimise human intervention.
    [Show full text]
  • Unsupervised Learning of Cross-Modal Mappings Between
    Unsupervised Learning of Cross-Modal Mappings between Speech and Text by Yu-An Chung B.S., National Taiwan University (2016) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 c Massachusetts Institute of Technology 2019. All rights reserved. Author.............................................................. Department of Electrical Engineering and Computer Science May 3, 2019 Certified by. James R. Glass Senior Research Scientist in Computer Science Thesis Supervisor Accepted by . Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students 2 Unsupervised Learning of Cross-Modal Mappings between Speech and Text by Yu-An Chung Submitted to the Department of Electrical Engineering and Computer Science on May 3, 2019, in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering and Computer Science Abstract Deep learning is one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications in computer vision, nat- ural language processing, and speech and audio processing. Current deep learning models, however, rely on significant amounts of supervision for training to achieve exceptional performance. For example, commercial speech recognition systems are usually trained on tens of thousands of hours of annotated data, which take the form of audio paired with transcriptions for training acoustic models, collections of text for training language models, and (possibly) linguist-crafted lexicons mapping words to their pronunciations. The immense cost of collecting these resources makes applying state-of-the-art speech recognition algorithm to under-resourced languages infeasible.
    [Show full text]
  • BITS: a Method for Bilingual Text Search Over the Web
    BITS: A Method for Bilingual Text Search over the Web Xiaoyi Ma, Mark Y. Liberman Linguistic Data Consortium 3615 Market St. Suite 200 Philadelphia, PA 19104, USA {xma,myl}@ldc.upenn.edu Abstract Parallel corpus are valuable based, only a few are statistical machine resource for machine translation, multi- translation. Some scholars believe that the lack lingual text retrieval, language education of large parallel corpora makes statistical and other applications, but for various approach impossible, the researchers don’t have reasons, its availability is very limited at large enough parallel corpuses to give some present. Noticed that the World Wide language pairs a shot. Web is a potential source to mine parallel However, the unexplored World Wide Web text, researchers are making their efforts could be a good resource to find large-size and to explore the Web in order to get a big balanced parallel text. According to the web collection of bitext. This paper presents survey we did in 1997, 1 of 10 de domain BITS (Bilingual Internet Text Search), a websites are German – English bilingual, the system which harvests multilingual texts number of de domain websites is about 150,000 over the World Wide Web with virtually at that time, so there might be 50,000 German – no human intervention. The technique is English bilingual websites in de domain alone. simple, easy to port to any language pairs, Things are changing since a potential gold mine and with high accuracy. The results of the of parallel text, the World Wide Web, has been experiments on German – English pair discovered.
    [Show full text]