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Using Bilingual Segments to Improve Interactive Machine Translation

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Natural Language Processing and Chinese Computing (NLPCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10619))

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Abstract

Recent research on machine translation has achieved substantial progress. However, the machine translation results are still not error-free, and need to be post-edited by a human translator (user) to produce correct translations. Interactive machine translation enhanced the human-computer collaboration through having human validate the longest correct prefix in the suggested translation. In this paper, we refine the interactivity protocol to provide more natural collaboration. Users are allowed to validate bilingual segments, which give more direct guidance to the decoder and more hints to the users. Besides, validating bilingual segments is easier than identifying correct segments from the incorrect translations. Experimental results with real users show that the new protocol improved the translation efficiency and translation quality on three Chinese-English translation tasks.

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Notes

  1. 1.

    http://ictclas.nlpir.org/.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61402299).

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Authors and Affiliations

  1. Human-Computer Intelligence Research Center, Shenyang Aerospace University, Shenyang, 110136, China

    Na Ye, Ping Xu, Chuang Wu & Guiping Zhang

Authors
  1. Na Ye
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  2. Ping Xu
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  3. Chuang Wu
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  4. Guiping Zhang
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Corresponding author

Correspondence to Na Ye .

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Editors and Affiliations

  1. Fudan University, Shanghai, China

    Xuanjing Huang

  2. Singapore Management University, Singapore, Singapore

    Jing Jiang

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

  5. Soochow University, Suzhou, China

    Yu Hong

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Ye, N., Xu, P., Wu, C., Zhang, G. (2018). Using Bilingual Segments to Improve Interactive Machine Translation. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-73618-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73617-4

  • Online ISBN: 978-3-319-73618-1

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