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National CCF Conference on Natural Language Processing and Chinese Computing

NLPCC 2017: Natural Language Processing and Chinese Computing pp 224–232Cite as

Modeling Indicative Context for Statistical Machine Translation

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

Abstract

Contextual information is very important to select the appropriate phrases in statistical machine translation (SMT). The selection of different target phrases is sensitive to different parts of source contexts. Previous approaches based on either local contexts or global contexts neglect impacts of different contexts and are not always effective to disambiguate translation candidates. As a matter of fact, the indicative contexts are expected to play more important roles for disambiguation. In this paper, we propose to leverage the indicative contexts for translation disambiguation. Our model assigns phrase pairs confidence scores based on different source contexts which are then intergraded into the SMT log-linear model to help select translation candidates. Experimental results show that our proposed method significantly improves translation performance on the NIST Chinese-to-English translation tasks compared with the state-of-the-art SMT baseline.

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Notes

  1. 1.

    LDC2003E14, LDC2005T10, LDC2005E83, LDC2006E26, LDC2006E34, LDC2006E85, LDC2006E92, LDC2003E07, LDC2005T06, LDC2004T08, LDC2005T06.

References

  1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR 2015 (2015)

    Google Scholar 

  2. Carpuat, M., Wu, D.: Word sense disambiguation vs. statistical machine translation. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 387–394. Association for Computational Linguistics (2005)

    Google Scholar 

  3. Chiang, D.: A hierarchical phrase-based model for statistical machine translation. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 263–270. Association for Computational Linguistics (2005)

    Google Scholar 

  4. Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R.M., Makhoul, J.: Fast and robust neural network joint models for statistical machine translation. In: ACL, vol. 1, pp. 1370–1380. Citeseer (2014)

    Google Scholar 

  5. Galley, M., Graehl, J., Knight, K., Marcu, D., DeNeefe, S., Wang, W., Thayer, I.: Scalable inference and training of context-rich syntactic translation models. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 961–968. Association for Computational Linguistics (2006)

    Google Scholar 

  6. He, Z., Liu, Q., Lin, S.: Improving statistical machine translation using lexicalized rule selection. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pp. 321–328. Association for Computational Linguistics (2008)

    Google Scholar 

  7. Hu, B., Tu, Z., Lu, Z., Li, H., Chen, Q.: Context-dependent translation selection using convolutional neural network. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 536–541. Association for Computational Linguistics, Beijing, July 2015. http://www.aclweb.org/anthology/P15-2088

  8. Huang, L., Chiang, D.: Better k-best parsing. In: Proceedings of the Ninth International Workshop on Parsing Technology, pp. 53–64. Association for Computational Linguistics (2005)

    Google Scholar 

  9. Jean, S., Cho, K., Memisevic, R., Bengio, Y.: On using very large target vocabulary for neural machine translation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1–10. Association for Computational Linguistics, Beijing, July 2015. http://www.aclweb.org/anthology/P15-1001

  10. Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: 1995 International Conference on Acoustics, Speech, and Signal Processing. ICASSP-1995, vol. 1, pp. 181–184. IEEE (1995)

    Google Scholar 

  11. Koehn, P.: Statistical significance tests for machine translation evaluation. In: EMNLP, pp. 388–395. Citeseer (2004)

    Google Scholar 

  12. Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, pp. 48–54. Association for Computational Linguistics (2003)

    Google Scholar 

  13. Liu, Q., He, Z., Liu, Y., Lin, S.: Maximum entropy based rule selection model for syntax-based statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 89–97. Association for Computational Linguistics (2008)

    Google Scholar 

  14. Liu, Y., Liu, Q., Lin, S.: Tree-to-string alignment template for statistical machine translation. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 609–616. Association for Computational Linguistics (2006)

    Google Scholar 

  15. Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)

  16. Marton, Y., Resnik, P.: Soft syntactic constraints for hierarchical phrased-based translation. In: ACL, pp. 1003–1011 (2008)

    Google Scholar 

  17. Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, pp. 160–167. Association for Computational Linguistics (2003)

    Google Scholar 

  18. Och, F.J., Ney, H.: Improved statistical alignment models. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 440–447. Association for Computational Linguistics (2000)

    Google Scholar 

  19. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)

    Google Scholar 

  20. Xiong, D., Zhang, M., Aw, A., Li, H.: A syntax-driven bracketing model for phrase-based translation. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1, pp. 315–323. Association for Computational Linguistics (2009)

    Google Scholar 

  21. Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)

  22. Zhang, J.: Local translation prediction with global sentence representation. arXiv preprint arXiv:1502.07920 (2015)

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Author information

Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Shuangzhi Wu

  2. Microsoft Research Asia, Bejing, China

    Dongdong Zhang, Shujie Liu & Ming Zhou

Authors
  1. Shuangzhi Wu
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  2. Dongdong Zhang
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  3. Shujie Liu
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  4. Ming Zhou
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Corresponding author

Correspondence to Shuangzhi Wu .

Editor information

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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Wu, S., Zhang, D., Liu, S., Zhou, M. (2018). Modeling Indicative Context for Statistical 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_19

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  • DOI: https://doi.org/10.1007/978-3-319-73618-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73617-4

  • Online ISBN: 978-3-319-73618-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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