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Terminology Translation Error Identification and Correction

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Social Media Processing (SMP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 774))

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Abstract

Statistical machine translation (SMT) system requires homogeneous training data in order to get domain-sensitive terminology translations. If the data is multi-domain mixed, it is difficult for SMT system to learn translation probability of context-sensitive terminology. However, terminology translation is important for SMT. The previous work mainly focuses on integrating terminology into machine translation systems and heavily relies on domain terminology resources. In this paper, we propose a back translation based method to identify terminology translation errors from SMT outputs and automatically suggest a better translation. Our approach is simple with no external resources and can be applied to any type of SMT system. We use three metrics: tree-edit distance, sentence semantic similarity and language model perplexity to measure the quality of back translation. Experimental results illustrate that our method improves performance on both weak and strong SMT systems, yielding a precision of 0.48% and 1.51% respectively.

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Notes

  1. 1.

    http://translate.google.cn.

  2. 2.

    http://www.statmt.org/moses.

  3. 3.

    http://fanyi.youdao.com/openapi?path=data-mode.

  4. 4.

    https://nlp.stanford.edu/software/nndep.shtml.

  5. 5.

    https://docs.python.org/3/library/urllib.html.

  6. 6.

    https://www.crummy.com/software/BeautifulSoup/.

  7. 7.

    http://opennlp.apache.org/.

  8. 8.

    LDC2003T09 Gigaword Chinese Text Corpus Second Edition.

  9. 9.

    LDC2009T13 Xinhua News Portion of English Gigaword Second Edition.

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Acknowledgments

This research work is supported by National Natural Science Foundation of China (Grants No. 61373097, No. 61672367, No. 61672368, No. 61331011), the Research Foundation of the Ministry of Education and China Mobile, MCM20150602 and the Science and Technology Plan of Jiangsu, SBK2015022101. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Mengyi Liu, Jian Tang, Yu Hong & Jianmin Yao

Authors
  1. Mengyi Liu
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  2. Jian Tang
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  3. Yu Hong
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  4. Jianmin Yao
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Corresponding author

Correspondence to Yu Hong .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xueqi Cheng

  2. Beijing Jinri Toutiao Technology Co. Ltd , Beijing, China

    Weiying Ma

  3. Arizona State University , Tempe, Arizona, USA

    Huan Liu

  4. Institute of Computing Technology, Chinese Academy of Sciences , Beijing, China

    Huawei Shen

  5. Renmin University of China , Beijing, China

    Shizheng Feng

  6. Microsoft Asia Research , Beijing, China

    Xing Xie

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Liu, M., Tang, J., Hong, Y., Yao, J. (2017). Terminology Translation Error Identification and Correction. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_12

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  • DOI: https://doi.org/10.1007/978-981-10-6805-8_12

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  • Print ISBN: 978-981-10-6804-1

  • Online ISBN: 978-981-10-6805-8

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