Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, Ann Arbor, MI, pp 65–72
Carl M, Way A (Eds) (2003) Recent advances in example-based machine translation. Kluwer Academic Publishers, Dordrecht, The Netherlands
MATH
Google Scholar
Choi H, Cho K, Bengio Y (2018) Fine-grained attention mechanism for neural machine translation. Neurocomputing 284:171–176
Article
Google Scholar
Chung J, Cho K, Bengio Y (2016) A character-level decoder without explicit segmentation for neural machine translation. In: Proceedings of the 54th annual meeting of the association for computational linguistics, (Vol 1: Long Papers), Berlin, Germany, pp 1693–1703
Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers), Minneapolis, MN, pp 4171–4186
Ding Y, Liu Y, Luan H, Sun M (2017) Visualizing and understanding neural machine translation. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: long papers), Vancouver, BC, pp 1150–1159
Ding S, Xu H, Koehn P (2019) Saliency-driven word alignment interpretation for neural machine translation. In: Proceedings of the fourth conference on machine translation (Volume 1: research papers), Florence, Italy, pp 1–12
Farajian MA, Turchi M, Negri M, Federico M (2017) Multi-domain neural machine translation through unsupervised adaptation. In: Proceedings of the second conference on machine translation, Copenhagen, Denmark, pp 127–137
Gal Y, Ghahramani Z (2016) A theoretically grounded application of dropout in recurrent neural networks. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 1027–1035
He D, Xia Y, Qin T, Wang L, Yu N, Tie-Yan Lu, Wei-Ying M (2016) Dual learning for machine translation. In: Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain, pp 820–828
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: long papers), Baltimore, MD, pp 655–665
Koehn P (2009) Statistical machine translation. Cambridge University Press, Cambridge, UK
Book
Google Scholar
Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: HLT-NAACL 2003: conference combining Human Language Technology conference series and the North American Chapter of the Association for Computational Linguistics conference series, Edmonton, AB, pp 48–54
Kudo T, Richardson J (2018) Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, Brussels, Belgium, pp 66–71
Lee J, Shin J-H, Kim J-S (2017) Interactive visualization and manipulation of attention-based neural machine translation. In: Proceedings of the 2017 conference on empirical methods in natural language processing: system demonstrations, Copenhagen, Denmark, pp 121–126
Ling W, Trancoso I, Dyer C, Black AW (2015) Character-based neural machine translation. arXiv:1511.04586
Läubli S, Sennrich R, Volk M (2018) Has machine translation achieved human parity? A case for document-level evaluation. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, pp 4791–4796
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781
Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, Philadelphia, PA, pp 311–318
Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha, Qatar, pp 1532–1543
Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of the 2018 conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, Volume 1 (long papers), New Orleans, LA, pp 2227–2237
Poncelas A, Shterionov D, Way A, de Buy Wenniger GM, Passban P (2018) Investigating backtranslation in neural machine translation. In: Proceedings of the 21st annual conference of the European Association for Machine Translation (EAMT 2018), Alicante, Spain, pp 249–258
Ruder S, Vulić I, Søgaard A (2019) A survey of cross-lingual word embedding models. J Artif Intell Res 65(1):569–631
MathSciNet
Article
Google Scholar
Sennrich R, Haddow B, Birch A (2016a) Neural machine translation of rare words with subword units. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: long papers), Berlin, Germany, pp 1715–1725
Sennrich R, Haddow B, Birch A (2016b) Improving neural machine translation models with monolingual data. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: long papers), Berlin, Germany, pp 86–96
Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th conference of the association for machine translation in the Americas, Austin, TX, pp 223–231
Sri Ram Kothur S, Knowles R, Koehn P (2018) Document-level adaptation for neural machine translation. In: Proceedings of the 2nd workshop on neural machine translation and generation, Melbourne, Australia, pp 64–73
Strobelt H, Gehrmann S, Behrisch M, Perer A, Pfister H, Rush AM (2018) Seq2seq-vis: A visual debugging tool for sequence-to-sequence models. IEEE Trans Visual Comp Graphics 25(1):353–363
Toral A, Castilho S, Hu K, Way A (2018) Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In: Proceedings of the third conference on machine translation: research papers, Brussels, Belgium, pp 113–123
Tran K, Bisazza A, Monz C (2016) Recurrent memory networks for language modeling. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, pp 321–331,
Tu Z, Lu Z, Liu Y, Liu X, Li H (2016) Modeling coverage for neural machine translation. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: long papers), Berlin, Germany, pp 76–85
van der Wees M, Bisazza A, Monz C (2017) Dynamic data selection for neural machine translation. In: Proceedings of the 2017 conference on empirical methods in natural language processing, Copenhagen, Denmark, pp 1400–1410
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, PolosukhinI (2017) Attention is all you need. In: Advances in neural information processing systems, Long Beach, CA, pp 6000–6010
Wang W, Peter JT, Rosendahl H, Ney H (2016) Character: Translation edit rate on character level. In: Proceedings of the first conference on machine translation: Volume 2, shared task papers, Berlin, Germany, pp 505–510
Way A (2018) Quality expectations of machine translation. In: Moorkens J, Castilho S, Gaspari F, Doherty S (Eds). Translation quality assessment: from principles to practice, Springer, Cham, Switzerland, pp 159–178
Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv:1409.2329
Zhang X, Kumar S, Khayrallah H, Murray K, Gwinnup J, Martindale MJ, McNamee P, Duh K, Carpuat M (2018) An empirical exploration of curriculum learning for neural machine translation. arXiv:1811.00739