logo

SCIENTIA SINICA Informationis, Volume 48 , Issue 5 : 574-588(2018) https://doi.org/10.1360/N112017-00222

Neural machine translation with constraints

More info
  • ReceivedMar 12, 2018
  • AcceptedApr 16, 2018
  • PublishedMay 11, 2018

Abstract


Funded by

国家自然科学基金优秀青年基金(61622209)


Acknowledgment

感谢本文所介绍工作的所有合作者


References

[1] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of Workshop on Neural Information Processing Systems, Montreal, 2014. 3104--3112. Google Scholar

[2] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. In: Proceedings of International Conference on Learning Representations (ICLR), San Diego, 2015. Google Scholar

[3] Koehn P. Statistical Machine Translation. Cambridge: Cambridge University Press, 2009. Google Scholar

[4] Xiong D Y, Zhang M. Linguistically Motivated Statistical Machine Translation: Models and Algorithms. Berlin: Springer, 2015. Google Scholar

[5] Och F J, Ney H. Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, 2002. 295--302. Google Scholar

[6] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, 2013. 3111--3119. Google Scholar

[7] Junczys-Dowmunt M, Dwojak T, Hoang H. Is neural machine translation ready for deployment? a case study on 30 translation directions. 2016,. arXiv Google Scholar

[8] Jean S, Firat O, Cho K, et al. Montreal neural machine translation systems for WMT15. In: Proceedings of the 10th Workshop on Statistical Machine Translation (WMT), Lisboa, 2015. 134--140. Google Scholar

[9] Wu Y H, Schuster M, Chen Z F, et al. Google's neural machine translation system: bridging the gap between human and machine translation. 2016,. arXiv Google Scholar

[10] Kuang S H, Xiong D Y. Automatic long sentence segmentation for neural machine translation. In: Proceedings of Conference on Natural Language Processing and Chinese Computing (NLPCC), Kunming, 2016. Google Scholar

[11] Jean S, Cho K, Memisevic R, et al. On using very large target vocabulary for neural machine translation. In: Proceedings of the 53rd Annual Metting on Association for Computational Linguistics (ACL), Beijing, 2015. Google Scholar

[12] Tu Z P, Lu Z D, Liu Y, et al. Modeling coverage for neural machine translation. In: Proceedings of the 54th Annual Metting on Association for Computational Linguistics (ACL), Berlin, 2016. 76--85. Google Scholar

[13] Kingma D R, Welling M. Auto-encoding variational bayes. In: Proceedings of International Conference on Learning Representations (ICLR), Banff, 2014. Google Scholar

[14] Rezende D J, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, 2014. 1278--1286. Google Scholar

[15] Kingma D P, Mohamed S, Rezende D J, et al. Semi-supervised learning with deep generative models. In: Proceedings of Conference on Neural Information Processing Systems (NIPS), Montreal, 2014. 3581--3589. Google Scholar

[16] Chung J Y, Kastner K, Dinh L, et al. A recurrent latent variable model for sequential data. In: Proceedings of Conference on Neural Information Processing Systems (NIPS), Montreal, 2015. 2980--2988. Google Scholar

[17] Miao Y S, Yu L, Blunsom P. Neural variational inference for text processing. In: Proceedings of the 33nd International Conference on Machine Learning (ICML), New York, 2016. 1727--1736. Google Scholar

[18] Bowman S R, Vilnis L, Vinyals O, et al. Generating sentences from a continuous space. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL), Berlin, 2016. 10--21. Google Scholar

[19] Li Z F, Eisner J, Khudanpur S. Variational decoding for statistical machine translation. In: Proceedings of the 47th Annual Meeting on Association for Computational Linguistics (ACL), Singapore, 2009. 593--601. Google Scholar

[20] He W, He Z J, Wu H, et al. Improved neural machine translation with SMT features. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, 2016. 151--157. Google Scholar

[21] Stahlberg F, Hasler E, Waite A, et al. Syntactically guided neural machine translation. In: Proceedings of the 54th Annual Metting on Association for Computational Linguistics (ACL), Berlin, 2016. 299--305. Google Scholar

[22] Arthur P, Neubig G, Nakamura S. Incorporating discrete translation lexicons into neural machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016. 1557--1567. Google Scholar

[23] Dahlmann L, Matusov E, Petrushkov P, et al. Neural machine translation leveraging phrase-based models in a hybrid search. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, 2017. 1411--1420. Google Scholar

[24] Niehues J, Cho E, Ha T L, et al. Pre-translation for neural machine translation. In: Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka, 2016. 1828--1836. Google Scholar

[25] Zhou L, Hu W P, Zhang J J, et al. Neural system combination for machine translation. In: Proceedings of Annual Meeting on Association for Computational Linguistics (ACL), Vancouver, 2017. 378--384. Google Scholar

[26] Eriguchi A, Hashimoto K, Tsuruoka Y. Tree-to-sequence attentional neural machine translation. In: Proceedings of the 54th Annual Metting on Association for Computational Linguistics (ACL), Berlin, 2016. 823--833. Google Scholar

[27] Sennrich R, Haddow B. Linguistic input features improve neural machine translation. In: Proceedings of the 1st Conference on Machine Translation, Berlin, 2016. 83--91. Google Scholar

[28] Shi X, Padhi I, Knight K. Does string-based neural MT learn source syntax? In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016. 1526--1534. Google Scholar

[29] Wu S Z, Zhang D D, Yang N, et al. Sequence-to-dependency neural machine translation. In: Proceedings of Annual Meeting on Association for Computational Linguistics (ACL), Vancouver, 2017. 698--707. Google Scholar

[30] Zhang B, Xiong D, Su J S, et al. Variational neural machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016. 521--530. Google Scholar

[31] Chung J Y, Gulcehre C, Cho J, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. In: Proceedings of NIPS Deep Learning and Representation Learning Workshop, Montreal, 2014. Google Scholar

[32] Luong M T, Sutskever I, Quoc V. Addressing the rare word problem in neural machine translation. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL), Beijing, 2015. 11--19. Google Scholar

[33] Wang X, Lu Z D, Tu Z P, et al. Neural machine translation advised by statistical machine translation. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, 2017. 3330--3336. Google Scholar

[34] Wang X, Tu Z P, Xiong D Y, et al. Translating phrases in neural machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, 2017. 1421--1431. Google Scholar

[35] Liu Y, Liu Q, Lin S X. Tree-to-string alignment template for statistical machine translation. In: Proceedings of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL), Sydney, 2006. 609--616. Google Scholar

[36] Shen L B, Xu J X, Weischedel R. A new string-to-dependency machine translation algorithm with a target dependency language model. In: Proceedings of the Annual Meeting on Association for Computational Linguistics with the Human Language Technology Conference (ACL-HLT), Columbus, 2008. 577--585. Google Scholar

[37] Xiong D Y, Liu Q, Lin S X. Maximum entropy based phrase reordering model for statistical machine translation. In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics (ACL), Sydney, 2006. 521--528. Google Scholar

[38] Xiong D Y, Liu Q, Lin S X. A dependency treelet string correspondence model for statistical machine translation. In: Proceedings of the 2nd Workshop on Statistical Machine Translation (WMT), Prague, 2007. 40--47. Google Scholar

[39] Li J H, Resnik P, Daumé H. Modeling syntactic and semantic structures in hierarchical phrase-based translation. In: Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Atlanta, 2013. 540--549. Google Scholar

[40] Marton Y, Resnik P. Soft syntactic constraints for hierarchical phrased-based translation. In: Proceedings of the Annual Meeting on Association for Computational Linguistics with the Human Language Technology Conference (ACL-HLT), Columbus, 2008. 1003--1011. Google Scholar

[41] Xiong D Y, Zhang M, Aw A. Linguistically annotated reordering: evaluation and analysis. Comput Linguist, 2010, 36: 535-568 CrossRef Google Scholar

[42] Li J H, Xiong D Y, Tu Z P, et al. Modeling source syntax for neural machine translation. In: Proceedings of Annual Meeting on Association for Computational Linguistics (ACL), Vancouver, 2017. 688--697. Google Scholar

[43] Choe D K, Charniak E. Parsing as language modeling. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016. 2331--2336. Google Scholar

[44] Vinyals O, Kaiser L, Koo T, et al. Grammar as a foreign language. In: Proceedings of Conference on Neural Information Processing Systems (NIPS), Montreal, 2015. Google Scholar

[45] Gehring J, Auli M, Grangier D, et al. A convolutional encoder model for neural machine translation. In: Proceedings of Annual Meeting on Association for Computational Linguistics (ACL), Vancouver, 2017. 123--135. Google Scholar

[46] Ashish V, Noam S, Niki P, et al. Attention is all you need. In: Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, 2017. 6000--6010. Google Scholar