DOCSLIB.ORG

Automatic Error Detection and Correction in Neural Machine Translation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatic Error Detection and Correction in Neural Machine Translation Automatic Error Detection and Correction in Neural Machine Translation A comparative study of Swedish to English and Greek to English Anthi Papadopoulou Uppsala universitet Department of Linguistics and Philology Master’s Programme in Language Technology Master’s Thesis in Language Technology June 10, 2019 Supervisors: Eva Pettersson, Uppsala University Anna Sågvall Hein, Convertus AB Sebastian Schleussner, Convertus AB Abstract Automatic detection and automatic correction of machine translation output are important steps to ensure an optimal quality of the final output. In this work, we compared the output of neural machine translation of two different language pairs, Swedish to English and Greek to English. This comparison was made using common machine translation metrics (BLEU, METEOR, TER) and syntax-related ones (POSBLEU, WPF, WER on POS classes). It was found that neither common metrics nor purely syntax-related ones were able to capture the quality of the machine translation output accurately, but the decomposition of WER over POS classes was the most informative one. A sample of each language was taken, so as to aid in the comparison between manual and automatic error categorization of five error categories, namely reordering errors, inflectional errors, missing and extra words, and incorrect lexical choices. Both Spearman’s r and Pearson’s r showed that there is a good correlation with human judgment with values above 0.9. Finally, based on the results of this error categorization, automatic post editing rules were implemented and applied, and their performance was checked against the sample, and the rest of the data set, showing varying results. The impact on the sample was greater, showing improvement in all metrics, while the impact on the rest of the data set was negative. An investigation of that, alongside the fact that correction was not possible for Greek due to extremely free reference translations and lack of error patterns in spoken speech, reinforced the belief that automatic post-editing is tightly connected to consistency in the reference translation, while also proving that in machine translation output handling, potentially more than one reference translations would be needed to ensure better results. Contents Acknowledgments5 1. Introduction6 1.1. Purpose................................6 1.2. Outline................................7 2. Background8 2.1. Machine Translation.........................8 2.1.1. Rule-Based Machine Translation...............8 2.1.2. Statistical Machine Translation...............8 2.1.3. Neural Networks and Neural Machine Translation.....9 2.1.4. Google Translate....................... 10 2.1.5. Comparison of NMT to Other MT Methods........ 10 2.2. NLP-Cube............................... 11 2.3. Evaluation of MT Output...................... 11 2.3.1. Human Evaluation...................... 11 2.3.2. Error Categorization..................... 12 2.3.3. Automatic Evaluation.................... 13 2.3.3.1. Common Metrics................. 13 2.3.3.2. Syntax-Oriented Evaluation Metrics....... 16 2.4. Automatic Post-Editing........................ 18 2.5. Convertus............................... 19 3. Data 20 3.1. Swedish to English (StE)....................... 20 3.2. Greek to English (GtE)........................ 20 4. Method 21 5. Evaluation 23 5.1. Common Metrics........................... 23 5.1.1. BLEU............................. 23 5.1.2. METEOR........................... 23 5.1.3. TER.............................. 23 5.1.4. Results and Short Discussion................ 23 5.2. Syntax-Oriented Metrics....................... 25 5.2.1. POSBLEU........................... 25 5.2.2. Decomposition of WER on POS classes.......... 26 5.2.3. WPF.............................. 27 5.2.4. Results and Short Discussion................ 28 5.3. Error Categorization......................... 31 5.3.1. Manual Error Categorization................ 32 3 5.3.2. Automatic Error Categorization............... 32 5.3.3. Results and Short Discussion................ 34 5.3.4. Spearman’s r and Peason’s r rank correlation coefficients. 38 5.3.5. Pipeline and HTML Visualization.............. 39 6. Automatic Post-Editing 40 6.1. GtE sample data........................... 40 6.2. StE sample data............................ 40 6.3. Results and Short Discussion..................... 41 6.4. APE on the Rest of the Data..................... 42 7. Conclusion and Future Work 45 A. Pipeline 48 B. HTML 49 4 Acknowledgments I am grateful to my academic supervisor Eva Pettersson, for sharing her knowledge with me, answering all my questions, and tirelessly correcting all of my typos. Special gratitude to my supervisors at Convertus, Anna Sågvall Hein and Sebastian Schleussner, for taking me on this thesis project, and providing me with valuable data, tools, and most importantly expertise. I would also like to thank my partner, family, and friends for being helpful, patient, and understanding with me throughout this period. 5 1. Introduction Neural networks (NN) have been studied and are being used in the field of computational linguistics, thus evolving a lot. In machine translation (MT), one can come across various techniques, from rule-based (RB), to statistical (SMT), to example based, and finally to approaches using the current ever so popular neural models for neural machine translation (NMT). No matter how advanced the procedures that are used are, though, there are always ungrammatical structures in the output, such as varying verb inflections or lack of source information, such as negations. Evaluating the output of this procedure and detecting errors (also known as quality estimation, QE) is undoubtedly a tiring and time-consuming activity for human evaluators. This is why one can observe a shift from manual human evaluation to a more automatic approach that allows re-use and faster results. The same effort is being put into the process of automatic post-editing (PE), which is considered vital in MT. Manual post-editing is considered tiring since MT systems tend to repeat the same mistakes over and over again. This is why it is more desirable to develop automatic post-editing mechanisms that will be informed of the errors detected and thus, result in a good output quality, while also minimizing the human factor in the procedure. The thesis project was carried out at Convertus AB, a machine translation services company in Uppsala, Sweden, which was created as a spin-off from Uppsala University. Convertus AB offers complete machine translation services for quality output, including automatic language checking and machine translation, as well as automatic and manual post-editing of the translated text. 1.1. Purpose The purpose of this thesis is to evaluate and detect errors in the output of neural machine translation from Swedish and Greek, to English, using different kinds of metrics, and to formulate and apply automatic post editing rules, so as to check the impact of them on the output quality. The tool NLP-Cube1 will be used to aid in the procedure of detecting ungram- matical structures, such as varying verb inflections, or lack of source information, such as negations, etc. Common quality metrics are to be calculated for the output, and then they are to be compared to syntax-related metrics, based on the part-of-speech (POS) information derived by the tool. After these errors are categorized, they are then passed to post-editing mechanisms which will be formulated to achieve a higher quality output. There are several research questions that this thesis wishes to address throughout the tasks: 1http://opensource.adobe.com/NLP-Cube/index.html 6 • How much do common evaluation metrics actually represent the quality of the MT output? • How can syntax aid in the process of evaluating MT output quality? • What types of errors exist in neural machine translation output? How can they be categorized manually? What about automatically? How do they differ for the Swedish-to-English and Greek-to-English pairs? • What types of rules could be formulated for the automatic post-editing step? Can they be common or do they have to be language specific? • What is the effect of these rules to the quality of the output? 1.2. Outline The outline of the thesis is the following. In section 2, a background of the study is given, which discusses neural machine translation and its comparison to statistical machine translation, followed by an introduction to NLP-Cube, the tool used in this study for grammatical analysis. After that studies proposing methods for error analysis based on POS-tags will be proposed, and common metrics for evaluation that are heavily used will be discussed. Following that, automatic PE will be presented. In section 3, the data used will be presented alongside the methods used, with the results being shown in section 4. An in-depth discussion of the results will be presented in section 5 followed by automatic PE efforts and their results in section 6. Finally in section 7, some suggestions for improvement and some guidance for future work will be given. 7 2. Background 2.1. Machine Translation Machine translation (MT) is the process of translating text from one language to another using a computer, and it has always been a growing area in the field of computational linguistics, dating back to the 1950’s. In its base form it substi- tutes words from the source language to the target language. As one can easily understand this cannot guarantee good translations, since a language does not only consist of words put one
Load more

Recommended publications
  • Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation
    Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 760301, 12 pages http://dx.doi.org/10.1155/2014/760301 Research Article Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation Aaron L.-F. Han, Derek F. Wong, Lidia S. Chao, Liangye He, and Yi Lu Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau Correspondence should be addressed to Aaron L.-F. Han; [email protected] Received 30 August 2013; Accepted 2 December 2013; Published 28 April 2014 Academic Editors: J. Shu and F. Yu Copyright © 2014 Aaron L.-F. Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the rapid development of machine translation (MT), the MT evaluation becomes very important to timely tell us whether the MT system makes any progress. The conventional MT evaluation methods tend to calculate the similarity between hypothesis translations offered by automatic translation systems and reference translations offered by professional translators. There are several weaknesses in existing evaluation metrics. Firstly, the designed incomprehensive factors result in language-bias problem, which means they perform well on some special language pairs but weak on other language pairs. Secondly, they tend to use no linguistic features or too many linguistic features, of which no usage of linguistic feature draws a lot of criticism from the linguists and too many linguistic features make the model weak in repeatability.
    [Show full text]
  • Arxiv:2108.09484V1 [Cs.CL] 21 Aug 2021
    cushLEPOR: Customised hLEPOR Metric Using LABSE Distilled Knowledge Model to Improve Agreement with Human Judgements Lifeng Han1, Irina Sorokina2, Gleb Erofeev2, and Serge Gladkoff2 1 ADAPT Research Centre, DCU, Ireland 2 Logrus Global, Translation & Localization gleberof, irina.sorokina, [email protected] Abstract aspects of timely and high quality evaluations, as well as reflecting the translation errors that MT Human evaluation has always been expen- systems can take advantages of for further improve- sive while researchers struggle to trust the automatic metrics. To address this, we pro- ment (Han et al., 2021b). On the one hand, human pose to customise traditional metrics by tak- evaluations have long been criticised as expensive ing advantages of the pre-trained language and unrepeatable. Furthermore, the inter and intra- models (PLMs) and the limited available hu- agreement levels from Human raters always strug- man labelled scores. We first re-introduce gle to achieve a relatively high and reliable score. the hLEPOR metric factors, followed by the On the other hand, automatic evaluation metrics Python portable version we developed which have reached high performances in the category achieved the automatic tuning of the weight- ing parameters in hLEPOR metric. Then of system level evaluations of MT systems (Fre- we present the customised hLEPOR (cush- itag et al., 2021). However, the segment level per- LEPOR) which uses LABSE distilled knowl- formance is still a large gap from human experts’ edge model to improve the metric agreement expectation. with human judgements by automatically op- In the meantime, many pre-trained language timised factor weights regarding the exact models have been proposed and developed in very MT language pairs that cushLEPOR is de- ployed to.
    [Show full text]
  • LEPOR: a Robust Evaluation Metric for Machine Translation with Augmented Factors
    LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors Aaron L. F. HAN Derek F. WONG Lidia S. CHAO Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory Department of Computer and Information Science University of Macau Macau S.A.R., China [email protected], [email protected], [email protected] ABSTRACT In the conventional evaluation metrics of machine translation, considering less information about the translations usually makes the result not reasonable and low correlation with human judgments. On the other hand, using many external linguistic resources and tools (e.g. Part-of- speech tagging, morpheme, stemming, and synonyms) makes the metrics complicated, time- consuming and not universal due to that different languages have the different linguistic features. This paper proposes a novel evaluation metric employing rich and augmented factors without relying on any additional resource or tool. Experiments show that this novel metric yields the state-of-the-art correlation with human judgments compared with classic metrics BLEU, TER, Meteor-1.3 and two latest metrics (AMBER and MP4IBM1), which proves it a robust one by employing a feature-rich and model-independent approach. KEYWORDS : Machine translation, Evaluation metric, Context-dependent n-gram alignment, Modified length penalty, Precision, Recall. Proceedings of COLING 2012: Posters, pages 441–450, COLING 2012, Mumbai, December 2012. 441 1 Introduction Since IBM proposed and realized the system of BLEU (Papineni et al., 2002) as the automatic metric for Machine Translation (MT) evaluation, many other methods have been proposed to revise or improve it. BLEU considered the n-gram precision and the penalty for translation which is shorter than that of references.
    [Show full text]
  • International Journal of Computer Sciences and Engineering Open Access Research Paper Vol-6, Special Issue-5, Jun 2018 E-ISSN: 2347-2693
    International Journal of Computer Sciences and Engineering Open Access Research Paper Vol-6, Special Issue-5, Jun 2018 E-ISSN: 2347-2693 A Study of metrics for evaluation of Machine translation K. Sourabh1, S. M Aaqib2, V. Mansotra3 1Dept. of Computer Science, GGM Science College, Jammu, India 2Dept. of Computer Science, Amar Singh Science College, Srinagar, India 3Dept. of Computer Science and IT, University of Jammu, Jammu, India Available online at: www.ijcseonline.org Abstract: Machine Translation has gained popularity over the years and has become one of the promising areas of research in computer science. Due to a consistent growth of internet users across the world information is now more versatile and dynamic available in almost all popular spoken languages throughout the world. From Indian perspective importance of machine translation become very obvious because Hindi is a language that is widely used across India and whole world. Many initiatives have been taken to facilitate Indian users so that information may be accessed in Hindi by converting it from one language to other. In this paper we have studied various available automatic metrics that evaluate the quality of translation correlation with human judgments. Keywords: Machine Translation, Corpus, bleu, Nist, Meteor, wer, ter, gtm. I. Introduction II. Manual Translation Evaluation Machine translation is a sub-field of computational Evaluation plays a very important role in examining the linguistics that investigates the use of software to translate quality of MT output. Manual evaluation is very time text or speech from one language to another. As internet is consuming and prejudiced, hence use of automatic metrics is now flooded with multilingual information for global made most of the times.
    [Show full text]
  • An Experimental Study of Automatic Metrics for Machine Translation Evaluation K.Sourabh1, S.M Aaqib2, Minu Bala3 1Assistant Professor, Dept
    An Experimental Study of Automatic Metrics for Machine Translation Evaluation K.Sourabh1, S.M Aaqib2, Minu Bala3 1Assistant Professor, Dept. of Computer Science, GGM Science College, Jammu 2Assistant Professor, Dept. of Computer Science, Amar Singh Science College, Srinagar 3Sr. Assistant Professor Dept. of Computer Science GGM Science College ABSTRACT Machine Translation has gained popularity over the years and has become one of the promising areas of research in computer science. Due to a consistent growth of internet users across the world information is now more versatile and dynamic available in almost all popular spoken languages throughout the world. From Indian perspective importance of machine translation become very obvious because Hindi is a language that is widely used across India and whole world. Many initiatives have been taken to facilitate Indian users so that information may be accessed in Hindi by converting it from one language to other. In this paper we have studied various available automatic metrics that evaluate the quality of translation correlation with human judgments. Also we have tested various automatic metrics available for their performance against human evaluation. Keywords: Machine Translation, Evaluation, Bleu, Meteor, Ter, GTM. I. INTRODUCTION Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. As internet is now flooded with multilingual information for global community, research and development giving space to Machine Evaluation plays a major role in the field of Natural Language Processing. Many MT tools like Google translate, Bing, Systran SDL etc are providing online services to translate text from one language to another.
    [Show full text]
  • LEPOR: an Augmented Machine Translation Evaluation Metric Lifeng
    LEPOR: An Augmented Machine Translation Evaluation Metric by Lifeng Han, Aaron Master of Science in Software Engineering 2014 Faculty of Science and Technology University of Macau LEPOR: AN AUGMENTED MACHINE TRANSLATION EVALUATION METRIC by LiFeng Han, Aaron A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering Faculty of Science and Technology University of Macau 2014 Approved by ___________________________________________________ Supervisor __________________________________________________ __________________________________________________ __________________________________________________ Date __________________________________________________________ In presenting this thesis in partial fulfillment of the requirements for a Master's degree at the University of Macau, I agree that the Library and the Faculty of Science and Technology shall make its copies freely available for inspection. However, reproduction of this thesis for any purposes or by any means shall not be allowed without my written permission. Authorization is sought by contacting the author at Address:FST Building 2023, University of Macau, Macau S.A.R. Telephone: +(00)853-63567608 E-mail: [email protected] Signature ______________________ 2014.07.10th Date __________________________ University of Macau Abstract LEPOR: AN AUGMENTED MACHINE TRANSLATION EVALUATION METRIC by LiFeng Han, Aaron Thesis Supervisors: Dr. Lidia S. Chao and Dr. Derek F. Wong Master of Science in Software Engineering Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement.
    [Show full text]
  • Translation Quality Assessment: a Brief Survey on Manual and Automatic Methods
    Translation Quality Assessment: A Brief Survey on Manual and Automatic Methods Lifeng Han1, Gareth J. F. Jones1, and Alan F. Smeaton2 1 ADAPT Research Centre 2 Insight Centre for Data Analytics School of Computing, Dublin City University, Dublin, Ireland [email protected] Abstract tical methods (Brown et al., 1993; Och and Ney, 2003; Chiang, 2005; Koehn, 2010), to the cur- To facilitate effective translation modeling rent paradigm of neural network structures (Cho and translation studies, one of the crucial et al., 2014; Johnson et al., 2016; Vaswani et al., questions to address is how to assess trans- 2017; Lample and Conneau, 2019), MT quality lation quality. From the perspectives of ac- continue to improve. However, as MT and transla- curacy, reliability, repeatability and cost, tion quality assessment (TQA) researchers report, translation quality assessment (TQA) it- MT outputs are still far from reaching human par- self is a rich and challenging task. In this ity (Läubli et al., 2018; Läubli et al., 2020; Han work, we present a high-level and con- et al., 2020a). MT quality assessment is thus still cise survey of TQA methods, including an important task to facilitate MT research itself, both manual judgement criteria and auto- and also for downstream applications. TQA re- mated evaluation metrics, which we clas- mains a challenging and difficult task because of sify into further detailed sub-categories. the richness, variety, and ambiguity phenomena of We hope that this work will be an asset natural language itself, e.g. the same concept can for both translation model researchers and be expressed in different word structures and pat- quality assessment researchers.
    [Show full text]
  • Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation
    Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 760301, 12 pages http://dx.doi.org/10.1155/2014/760301 Research Article Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation Aaron L.-F. Han, Derek F. Wong, Lidia S. Chao, Liangye He, and Yi Lu Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau Correspondence should be addressed to Aaron L.-F. Han; [email protected] Received 30 August 2013; Accepted 2 December 2013; Published 28 April 2014 Academic Editors: J. Shu and F. Yu Copyright © 2014 Aaron L.-F. Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the rapid development of machine translation (MT), the MT evaluation becomes very important to timely tell us whether the MT system makes any progress. The conventional MT evaluation methods tend to calculate the similarity between hypothesis translations offered by automatic translation systems and reference translations offered by professional translators. There are several weaknesses in existing evaluation metrics. Firstly, the designed incomprehensive factors result in language-bias problem, which means they perform well on some special language pairs but weak on other language pairs. Secondly, they tend to use no linguistic features or too many linguistic features, of which no usage of linguistic feature draws a lot of criticism from the linguists and too many linguistic features make the model weak in repeatability.
    [Show full text]
  • A Critical Analysis of Metrics Used for Measuring Progress in Artificial Intelligence
    A critical analysis of metrics used for measuring progress in artificial intelligence Kathrin Blagec 1, Georg Dorffner 1, Milad Moradi 1, Matthias Samwald 1* * Corresponding author Affiliations 1 Institute for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria. Abstract Comparing model performances on benchmark datasets is an integral part of measuring and driving progress in artificial intelligence. A model’s performance on a benchmark dataset is commonly assessed based on a single or a small set of performance metrics. While this enables quick comparisons, it may also entail the risk of inadequately reflecting model performance if the metric does not sufficiently cover all performance characteristics. Currently, it is unknown to what extent this might impact current benchmarking efforts. To address this question, we analysed the current landscape of performance metrics based on data covering 3867 machine learning model performance results from the web-based open platform ‘Papers with Code’. Our results suggest that the large majority of metrics currently used to evaluate classification AI benchmark tasks have properties that may result in an inadequate reflection of a classifiers’ performance, especially when used with imbalanced datasets. While alternative metrics that address problematic properties have been proposed, they are currently rarely applied as performance metrics in benchmarking tasks. Finally, we noticed that the reporting of metrics was partly inconsistent and partly unspecific, which may lead to ambiguities when comparing model performances. Introduction Benchmarking, i.e. the process of measuring and comparing model performance on a specific task or set of tasks, is an important driver of progress in artificial intelligence research.
    [Show full text]