Skip to main content
SpringerLink
Log in

Research for Uyghur-Chinese Neural Machine Translation

  • Conference paper
  • First Online:
Natural Language Understanding and Intelligent Applications (ICCPOL 2016, NLPCC 2016)

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

Abstract

The problem of rare and unknown words is an important issue in Uyghur-Chinese machine translation, especially using neural machine translation model. We propose a novel way to deal with the rare and unknown words. Based on neural machine translation of using pointers over input sequence, our approach which consists of preprocess and post-process can be used in all neural machine translation model. Pre-process modify the Uyghur-Chinese corpus to extend the ability of pointer network, and the post- process retranslating the raw translation by a phrase-based machine translation model or a wordlist. Experiment show that neural machine translation model used the approach proposed by this paper get a higher BLEU score than the phrase-based model in Uyghur-Chinese MT.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, Y., Wong, D.F., Chao, L,S,, et al.: The report of NLP 2 CT for CWMT 2015 evaluation task. In: The China Workshop on Machine Translation (2015)

    Google Scholar 

  2. Xiao, L., Tonghai, J., Xi, Z., et al.: An overview of research on the key technology of Uyghur-Chinese machine translation. Netw. New Media Technol. 5(01), 19–25 (2016)

    Google Scholar 

  3. Chen, K.: Research on the key technology of reordering model in Uyghur-Chinese machine translation. University of Chinese Academy of Sciences (2013)

    Google Scholar 

  4. Kong, J., Yang, Y., Wang, L., et al.: Research in corpus filtering technique for Uyghur Chinese Machine Translation. Appl. Res. Comput. 33 (2016)

    Google Scholar 

  5. Aisha, B., Sun, M.: Uyghur-Chinese statistical machine translation by incorporating morphological information. J. Comput. Inf. Syst. 6, 3137–3145 (2010)

    Google Scholar 

  6. Mi, C., Yang, Y., Zhou, X., et al.: A phrase table filtering model based on binary classification for Uyghur-Chinese machine translation. J. Comput. 9(12), 2780–2786 (2014)

    Google Scholar 

  7. Murat, A., Yusup, A., Abaydulla, Y.: Research and implementation of the Uyghur-Chinese personal name transliteration based on syllabification. In: International Conference on Asian Language Processing, pp. 71–74. IEEE (2013)

    Google Scholar 

  8. Devlin, J., Zbib, R., Huang, Z., et al.: Fast and robust neural network joint models for statistical machine translation. In: Meeting of the Association for Computational Linguistics, pp. 1370–1380 (2014)

    Google Scholar 

  9. Zou, W.Y., Socher, R., Cer, D., et al.: Bilingual word embeddings for phrase-based machine translation. In: Empirical Methods in Natural Language Processing (2013)

    Google Scholar 

  10. Cho, K., Van Merrienboer, B., Gulcehre, C., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Empirical Methods in Natural Language Processing (2014)

    Google Scholar 

  11. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Computer Science (2014)

    Google Scholar 

  12. Yang, Y., Mi, C., Ma, B., Dong, R., Wang, L., Li, X.: Character tagging-based word segmentation for Uyghur. In: Shi, X., Chen, Y. (eds.) CWMT 2014. CCIS, vol. 493, pp. 61–69. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45701-6_6

    Google Scholar 

  13. Gulcehre, C., Ahn, S., Nallapati, R., et al.: Pointing the unknown words. Computer Science (2016)

    Google Scholar 

  14. Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. Computer Science (2015)

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Young Creative Sci-Tech Talents Cultivation Project of Xinjiang Uyghur Autonomous Region (2014711006, 2014721032), the Natural Science Foundation of Xinjiang (2015211B034), the Xinjiang Key Laboratory Fund under Grant No. 2015KL031 and the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA06030400.

Author information

Authors and Affiliations

  1. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China

    Jinying Kong, Yating Yang, Xi Zhou, Lei Wang & Xiao Li

  2. Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China

    Jinying Kong, Yating Yang, Xi Zhou, Lei Wang & Xiao Li

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jinying Kong

  4. Institute of Acoustics Chinese Academy of Sciences, Beijing, China

    Yating Yang

Authors
  1. Jinying Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yating Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xi Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yating Yang .

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

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Kong, J., Yang, Y., Zhou, X., Wang, L., Li, X. (2016). Research for Uyghur-Chinese Neural Machine Translation. 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_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50496-4_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50495-7

  • Online ISBN: 978-3-319-50496-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation