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Input Method for Human Translators: A Novel Approach to Integrate Machine Translation Effectively and Imperceptibly

Published: 12 November 2018 Publication History

Abstract

Computer-aided translation (CAT) systems are the most popular tool for helping human translators efficiently perform language translation. To further improve the translation efficiency, there is an increasing interest in applying machine translation (MT) technology to upgrade CAT. To thoroughly integrate MT into CAT systems, in this article, we propose a novel approach: a new input method that makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses, and n-best translation lists. The proposed input method contains two parts: a phrase generation model, allowing human translators to type target sentences quickly, and an n-gram prediction model, helping users choose perfect MT fragments smoothly. In addition, to tune the underlying MT system to generate the input method preferable results, we design a new evaluation metric for the MT system. The proposed input method integrates MT effectively and imperceptibly, and it is particularly suitable for many target languages with complex characters, such as Chinese and Japanese. The extensive experiments demonstrate that our method saves more than 23% in time and over 42% in keystrokes, and it also improves the translation quality by more than 5 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.

References

[1]
Sergio Barrachina, Oliver Bender, Francisco Casacuberta, Jorge Civera, Elsa Cubel, Shahram Khadivi, Antonio Lagarda, Hermann Ney, Jesús Tomás, Enrique Vidal, and others. 2009. Statistical approaches to computer-assisted translation. Comput. Ling. 35, 1 (2009), 3--28.
[2]
Michael Carl, Barbara Dragsted, Jakob Elming, Daniel Hardt, and Arnt Lykke Jakobsen. 2011. The process of post-editing: A pilot study. In Proceedings of the 8th International NLPSC Workshop. Special Theme: Human-machine Interaction in Translation, Vol. 41. 131--142.
[3]
Shanbo Cheng, Shujian Huang, Huadong Chen, Xinyu Dai, and Jiajun Chen. 2016. PRIMT: A pick-revise framework for interactive machine translation. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’16). 1240--1249.
[4]
Wei Cui. 1985. Evaluation of chinese character keyboards. IEEE Compu. 18, 1 (1985), 54--63.
[5]
Ruiyu Fang and Xiaodong Shi. 2013. Research and Implementation on Aided Translation Tools Based on Input Method. Master’s thesis. Xiamen University.
[6]
George Foster and Guy Lapalme. 2002. Text Prediction for Translators. Ph.D. Dissertation. Université de Montréal.
[7]
Nestor Garay-Vitoria and Julio Abascal. 2006. Text prediction systems: A survey. Univers. Access Inf. Soc. 4, 3 (2006), 188--203.
[8]
Spence Green, Sida I. Wang, Jason Chuang, Jeffrey Heer, Sebastian Schuster, and Christopher D. Manning. 2014. Human effort and machine learnability in computer aided translation. In Proceeding of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1225--1236.
[9]
Guoping Huang, Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2015. A new input method for human translators: Integrating machine translation effectively and imperceptibly. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). 1163--1169.
[10]
Guoping Huang, Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2016a. Learning from user feedback for machine translation in real-time. In Proceedings of the 5th Conference on Natural Language Processing and Chinese Computing and the 24h International Conference on Computer Processing of Oriental Languages (NLPCC’16). 595--607.
[11]
Guoping Huang, Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2016b. A simple, straightforward and effective model for joint bilingual terms detection and word alignment in SMT. In Proceedings of the 5th Conference on Natural Language Processing and Chinese Computing and the 24th International Conference on Computer Processing of Oriental Languages (NLPCC’16). 103--115.
[12]
Guoping Huang, Chunlu Zhao, Hongyuan Ma, Yu Zhou, and Jiajun Zhang. 2016c. MinKSR: A novel MT evaluation metric for coordinating human translators with the CAT-oriented input method. In Proceedings of the 12th China Workshop on Machine Translation (CWMT’16). 1--13.
[13]
Tadao Kasami. 1965. An Efficient Recognition and Syntax Analysis Algorithm for Context-free Languages. Technical Report Technical Report AFCRL-65-758. Air Force Cambridge Research Laboratory.
[14]
Philipp Koehn. 2004. Statistical significance tests for machine translation evaluation. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP’04). 388--395.
[15]
Philipp Koehn. 2009a. A process study of computer-aided translation. Mach. Transl. 23, 4 (2009), 241--263.
[16]
Philipp Koehn. 2009b. A web-based interactive computer aided translation tool. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP’09). 17--20.
[17]
Philipp Koehn. 2012. Computer-added Trasnlation. (2012). Machine Translation Marathon. http://www.mt-archive.info/MTMarathon-2012-Koehn-ppt.pdf.
[18]
Philipp Koehn, Chara Tsoukala, and Herve Saint-Amand. 2014. Refinements to interactive translation prediction based on search graphs. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14). 574--578.
[19]
Dong Li. 2012. A pinyin input method editor with english-chinese aided translation function. In Proceedings of the 2012 International Conference on Computer Science and Service System. 446--449.
[20]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL’02). 311--318.
[21]
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of Association for Machine Translation in the Americas 2006, Vol. 200. 223--231.
[22]
Deyi Xiong, Qun Liu, and Shouxun Lin. 2006. Maximum entropy based phrase reordering model for statistical machine translation. In Proceedings of the International Conference on Computational Linguistics and the Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2’06). 521--528.
[23]
Daniel H. Younger. 1967. Recognition and parsing of context-free languages in time n<sup>3</sup>. Inf. Contr. 10, 2 (1967), 189--208.
[24]
Omar Zaidan. 2009. Z-MERT: A fully configurable open source tool for minimum error rate training of machine translation systems. Prague Bull. Math. Ling. 91 (2009), 79--88.
[25]
Ventsislav Zhechev. 2012. Machine translation infrastructure and post-editing performance at Autodesk. In Proceedings of the AMTA 2012 Workshop on Post-Editing Technology and Practice (WPTP’12). 87--96.

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      Published In

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 18, Issue 1
      March 2019
      196 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3292011
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 12 November 2018
      Accepted: 01 May 2018
      Revised: 01 March 2018
      Received: 01 November 2016
      Published in TALLIP Volume 18, Issue 1

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      Author Tags

      1. Machine translation
      2. computer-aided translation
      3. evaluation metric
      4. input method

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      • Refereed

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      • National Key Research and Development Program of China
      • Natural Science Foundation of China

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      View all
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      • (2022)An Assistant System for Translation Flipped ClassroomApplied Sciences10.3390/app1301032713:1(327)Online publication date: 27-Dec-2022
      • (2022)Singlish Checker: A Tool for Understanding and Analysing an English Creole LanguageFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_9(115-124)Online publication date: 7-Dec-2022
      • (2021)Machine Translation and Computer Aided English TranslationJournal of Physics: Conference Series10.1088/1742-6596/1881/4/0420231881:4(042023)Online publication date: 1-Apr-2021

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