The Impact of Named Entity Translation for Neural Machine Translation

  • Jinghui Yan
  • Jiajun Zhang
  • JinAn Xu
  • Chengqing Zong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 954)


Named entity translation has been shown in many studies that could have positive impact on performance of sentence level neural machine translation. In this paper, we study a mainstream structure that incorporating an external named entity translation model to neural machine translation. We give several comparison experiments by applying different named entity translation model structures, to clearly represent the impact of this structure in improving quality of neural machine translation. The experiments show that the proposed approach is able to achieve posistive result on some datasets and we give our analysis of influence factors.


Named entity Neural machine translation Named entity translation 



The research work described in this paper has been supported by the National Key Research and Development Program of China under Grant No. 2016QY02D0303 and the Natural Science Foundation of China under Grant No. 61673380.


  1. 1.
    Kalchbrenner, N., Blunsom, P.: Recurrent continuous translation models. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1700–1709 (2013)Google Scholar
  2. 2.
    Zhang, J., Zong, C., Li, S.: Sentence type based reordering model for statistical machine translation. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pp. 1089–1096. Association for Computational Linguistics (2008)Google Scholar
  3. 3.
    Zhang J., Zong, C.: Bridging neural machine translation and bilingual dictionaries. arXiv preprint arXiv:1610.07272 (2016)
  4. 4.
    Cambria, E., Hussain, A., Durrani, T., Zhang, J.: Towards a Chinese common and common sense knowledge base for sentiment analysis. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) IEA/AIE 2012. LNCS (LNAI), vol. 7345, pp. 437–446. Springer, Heidelberg (2012). Scholar
  5. 5.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  6. 6.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  7. 7.
    Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909 (2015)
  8. 8.
    Gage, P.: A new algorithm for data compression. C Users J. 12(2), 23–38 (1994)Google Scholar
  9. 9.
    Wang, Y., Zhou, L., Zhang, J., Zong, C.: Word, subword or character? An empirical study of granularity in Chinese-English NMT. In: Wong, D.F., Xiong, D. (eds.) CWMT 2017. CCIS, vol. 787, pp. 30–42. Springer, Singapore (2017). Scholar
  10. 10.
    Li, X., Zhang, J., Zong, C.: Neural name translation improves neural machine translation. arXiv preprint arXiv:1607.01856 (2016)
  11. 11.
    Luong, M.T., Sutskever, I., Le, Q.V., Vinyals, O., Zaremba, W.: Addressing the rare word problem in neural machine translation. arXiv preprint arXiv:1410.8206 (2014)
  12. 12.
    Wan, S., Verspoor, C.M.: Automatic English-Chinese name transliteration for development of multilingual resources. In: Proceedings of the 17th International Conference on Computational Linguistics-Volume 2, pp. 1352–1356. Association for Computational Linguistics (1998)Google Scholar
  13. 13.
    Li, H., Zhang, M., Su, J.: A joint source-channel model for machine transliteration. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp. 159–166. Association for Computational Linguistics (2004)Google Scholar
  14. 14.
    Ekbal, A., Naskar, S.K., Bandyopadhyay, S.: A modified joint source-channel model for transliteration. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 191–198. Association for Computational Linguistics (2006)Google Scholar
  15. 15.
    Yang, D., Dixon, P., Pan, Y.C., Oonishi, T., Nakamura, M., Furui, S.: Combining a two-step conditional random field model and a joint source channel model for machine transliteration. In: Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration, pp. 72–75. Association for Computational Linguistics (2009)Google Scholar
  16. 16.
    Li, Z., Chng, E.S., Li, H.: Named entity transliteration with sequence-to-sequence neural network. In: 2017 International Conference on Proceedings of the Asian Language Processing (IALP), pp. 374–378. IEEE (2017)Google Scholar
  17. 17.
    Wang, Y., et al.: Sogou neural machine translation systems for WMT17. In: Proceedings of the Second Conference on Machine Translation, pp. 410–415 (2017)Google Scholar
  18. 18.
    Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)Google Scholar
  19. 19.
    Chen, Y., Zong, C., Su, K., et al.: Joint Chinese-English named entity recognition and alignment. Chin. J. Comput. 34(9), 1688–1696 (2011)CrossRefGoogle Scholar
  20. 20.
    Huang, S.: LDC2005T34: Chinese \(<\)-\(>\) English named entity lists v 1.0. Linguistics Data Consortium (2005)Google Scholar
  21. 21.
    Kingma, D.P., Ba, J.: ADAM: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jinghui Yan
    • 1
  • Jiajun Zhang
    • 2
    • 3
  • JinAn Xu
    • 1
  • Chengqing Zong
    • 2
    • 3
    • 4
  1. 1.Beijing Jiaotong UniversityBeijingChina
  2. 2.National Laboratory of Pattern RecognitionInstitute of Automation, CASBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

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