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A Simple, Straightforward and Effective Model for Joint Bilingual Terms Detection and Word Alignment in SMT

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Book cover Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10102))

Abstract

Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific statistical machine translation (SMT) tasks. However, it’s a challenging task to extract term translation knowledge from parallel sentences because of the error propagation in the SMT training pipeline. In this paper, we propose a simple, straightforward and effective model to mitigate the error propagation and improve the quality of term translation. The proposed model goes from initial weak monolingual detection of terms based on naturally annotated resources (e.g. Wikipedia) to a stronger bilingual joint detection of terms, and allows the word alignment to interact. The extensive experiments show that our method substantially boosts the performance of bilingual term detection by more than 8 points absolute F-score. And the term translation quality is substantially improved by more than 3.66% accuracy, as well as the sentence translation quality is significantly improved by 0.38 absolute BLEU points, compared with the strong baseline, i.e. the well tuned Moses.

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Notes

  1. 1.

    In this paper, we do not consider named entities (e.g., person names, location names, organization names, time and numbers) and treat named entities non-terms.

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Acknowledgments

The research work has been funded by the Natural Science Foundation of China under Grant No. 61403379.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Guoping Huang, Jiajun Zhang, Yu Zhou & Chengqing Zong

  2. University of Chinese Academy of Sciences, Beijing, China

    Guoping Huang

Authors
  1. Guoping Huang
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  2. Jiajun Zhang
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  3. Yu Zhou
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  4. Chengqing Zong
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Corresponding author

Correspondence to Chengqing Zong .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, China

    Chin-Yew Lin

  2. Brandeis University, Waltham, Massachusetts, USA

    Nianwen Xue

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Fudan University, Shanghai, China

    Xuanjing Huang

  5. Peking University, Beijing, China

    Yansong Feng

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Huang, G., Zhang, J., Zhou, Y., Zong, C. (2016). A Simple, Straightforward and Effective Model for Joint Bilingual Terms Detection and Word Alignment in SMT. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-50496-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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