Open Access
Issue
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 02038
Number of page(s) 9
Section Algorithm Optimization and Application
DOI https://doi.org/10.1051/itmconf/20224702038
Published online 23 June 2022
  1. FENG Y, SHAO C Z. Frontiers in Neural Machine Translation: A Literature Review[J], Journal of Chinese information Processing,2020,34(07):1-18. [Google Scholar]
  2. GAO M H, YU Z Q. A summary review of neural machine translation[J]. Journal of Yunnan Nationalities University (Natural Sciences Edition), 2019, 28(01):72-76. [Google Scholar]
  3. LI Y C,XIONG D Y,ZHANG M. A survey of neural machine translation[J]. Chinese Journal of Comput-ers,2018,41(12):2734-2755. [Google Scholar]
  4. Kalchbrenner N, Blunsom P. Recurrent continuous translation models. 2013. [Google Scholar]
  5. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Advances in neural information processing systems. 2017: 5998-6008. [Google Scholar]
  6. Shaw P, Uszkoreit J, Vaswani A. Self-attention with relative position representations[J]. arXiv preprint arXiv:1803.02155, 2018. [Google Scholar]
  7. LIU Y. Recent advances in neural machine teanslation[J]. Journal of Computer Research and Development,2017,54(6):1144. [Google Scholar]
  8. Eriguchi A, Hashimoto K, Tsuruoka Y. Tree-to-Sequence Attentional Neural Machine Translation[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016. [Google Scholar]
  9. Robinson J J. Dependency structures and transformational rules[J]. Language, 1970: 259-285. [CrossRef] [Google Scholar]
  10. Mikolov T, Chen K, Corrado G, et al. Efficient Estimation of Word Representations in Vector Space[J]. Computer Science, 2013. [Google Scholar]
  11. Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural computation, 1997, 9(8): 1735-1780. [CrossRef] [Google Scholar]
  12. Gehring J, Auli M, Grangier D, et al. Convolutional sequence to sequence learning[C]//International Conference on Machine Learning. PMLR, 2017: 1243-1252. [Google Scholar]
  13. Wang X, Tu Z, Wang L, et al. Self-attention with structural position representations [J]. arXiv preprint arXiv:1909.00383, 2019. [Google Scholar]
  14. Chen K, Wang R, Utiyama M, et al. Recurrent positional embedding for neural machine translation[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019: 1361-1367. [Google Scholar]
  15. Liu X, Yu H F, Dhillon I, et al. Learning to encode position for transformer with continuous dynamical model[C]//International Conference on Machine Learning. PMLR, 2020: 6327-6335. [Google Scholar]
  16. Li J, Xiong D, Tu Z, et al. Modeling source syntax for neural machine translation[J]. arXiv preprint arXiv:1705.01020, 2017. [Google Scholar]
  17. Sennrich R, Haddow B. Linguistic input features improve neural machine translation [J]. arXiv preprint arXiv:1606.02892, 2016. [Google Scholar]
  18. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. [Google Scholar]
  19. Ba J L, Kiros J R, Hinton G E. Layer normalization [J]. arXiv preprint arXiv:1607.06450, 2016. [Google Scholar]
  20. AN J. Machine translation of long English sentence based on dependency parsing and sequence labeling [J]. Journal of Lanzhou University of Technology,2018,1. [Google Scholar]
  21. LI Z H. Research on Neural Machine Translation Combining Lexicology And Syntax[D]. Shanghai Tiao Tong University, 2019. [Google Scholar]
  22. Kingma D P, Ba J. Adam: A method for stochastic optimization [J]. arXiv preprint arXiv:1412.6980, 2014. [Google Scholar]
  23. Papineni K, Roukos S, Ward T, et al. Bleu: a method for automatic evaluation of machine translation[C]//Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 2002: 311-318. [Google Scholar]
  24. Wu Y, Schuster M, Chen Z, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation [J]. arXiv preprint arXiv:1609.08144,2016. [Google Scholar]

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