Bibliography
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural Machine Translation By Jointly Learning To Align and Translate. In ICLR, 1–15. 2014. URL: https://arxiv.org/abs/1409.0473, arXiv:1409.0473.
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. Generating sentences from a continuous space. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, August 11-12, 2016, 10–21. 2016. URL: http://aclweb.org/anthology/K/K16/K16-1002.pdf.
Bram Bulte and Arda Tezcan. Neural fuzzy repair: integrating fuzzy matches into neural machine translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1800–1809. Florence, Italy, July 2019. Association for Computational Linguistics. URL: https://aclanthology.org/P19-1175, doi:10.18653/v1/P19-1175.
William Chan, Navdeep Jaitly, Quoc V. Le, and Oriol Vinyals. Listen, attend and spell. CoRR, 2015. URL: http://arxiv.org/abs/1508.01211.
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proc of EMNLP. 2014.
Sumit Chopra, Michael Auli, Alexander M Rush, and SEAS Harvard. Abstractive sentence summarization with attentive recurrent neural networks. Proceedings of NAACL-HLT16, pages 93–98, 2016.
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
Josep Crego, Jungi Kim, and Jean Senellart. Systran's pure neural machine translation system. arXiv preprint arXiv:1602.06023, 2016.
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Andrew Senior, Paul Tucker, Ke Yang, Quoc V Le, and others. Large scale distributed deep networks. In Advances in neural information processing systems, 1223–1231. 2012.
Yuntian Deng, Anssi Kanervisto, and Alexander M. Rush. What you get is what you see: A visual markup decompiler. CoRR, 2016. URL: http://arxiv.org/abs/1609.04938.
Chris Dyer, Jonathan Weese, Hendra Setiawan, Adam Lopez, Ferhan Ture, Vladimir Eidelman, Juri Ganitkevitch, Phil Blunsom, and Philip Resnik. Cdec: a decoder, alignment, and learning framework for finite-state and context-free translation models. In Proc ACL, 7–12. Association for Computational Linguistics, 2010.
Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy, and Matthias Paulik. Jointly learning to align and translate with transformer models. In Conference on Empirical Methods in Natural Language Processing (EMNLP). Hong Kong, November 2019. URL: https://arxiv.org/abs/1909.02074.
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence to sequence learning. CoRR, 2017. URL: http://arxiv.org/abs/1705.03122, arXiv:1705.03122.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, and others. Moses: open source toolkit for statistical machine translation. In Proc ACL, 177–180. Association for Computational Linguistics, 2007.
Tao Lei, Yu Zhang, and Yoav Artzi. Training rnns as fast as cnns. CoRR, 2017. URL: http://arxiv.org/abs/1709.02755, arXiv:1709.02755.
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. Gated graph sequence neural networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. 2016. URL: http://arxiv.org/abs/1511.05493.
Yang Liu and Mirella Lapata. Learning structured text representations. CoRR, 2017. URL: http://arxiv.org/abs/1705.09207, arXiv:1705.09207.
Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. Effective Approaches to Attention-based Neural Machine Translation. In Proc of EMNLP. 2015.
Minh-Thang Luong, Ilya Sutskever, Quoc Le, Oriol Vinyals, and Wojciech Zaremba. Addressing the Rare Word Problem in Neural Machine Translation. In Proc of ACL. 2015.
Nicholas Léonard, Sagar Waghmare, Yang Wang, and Jin-Hwa Kim. Rnn : recurrent library for torch. CoRR, 2015. URL: http://arxiv.org/abs/1511.07889.
André FT Martins and Ramón Fernandez Astudillo. From softmax to sparsemax: a sparse model of attention and multi-label classification. arXiv preprint arXiv:1602.02068, 2016.
André F. T. Martins and Ramón Fernández Astudillo. From softmax to sparsemax: A sparse model of attention and multi-label classification. CoRR, 2016. URL: http://arxiv.org/abs/1602.02068, arXiv:1602.02068.
Graham Neubig. Travatar: a forest-to-string machine translation engine based on tree transducers. In Proc ACL. Sofia, Bulgaria, August 2013.
Abigail See, Peter J. Liu, and Christopher D. Manning. Get to the point: summarization with pointer-generator networks. CoRR, 2017. URL: http://arxiv.org/abs/1704.04368, arXiv:1704.04368.
Rico Sennrich and Barry Haddow. Linguistic input features improve neural machine translation. arXiv preprint arXiv:1606.02892, 2016.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. CoRR, 2015. URL: http://arxiv.org/abs/1508.07909.
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to Sequence Learning with Neural Networks. In NIPS, 9. 2014. URL: http://arxiv.org/abs/1409.3215, arXiv:1409.3215.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, 2017. URL: http://arxiv.org/abs/1706.03762, arXiv:1706.03762.
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. Matching networks for one shot learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, 3630–3638. 2016. URL: http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.
Oriol Vinyals and Quoc Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015.
Qiang Wang, Bei Li, Tong Xiao, Jingbo Zhu, Changliang Li, Derek F. Wong, and Lidia S. Chao. Learning deep transformer models for machine translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1810–1822. Florence, Italy, July 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P19-1176, doi:10.18653/v1/P19-1176.
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. Switchout: an efficient data augmentation algorithm for neural machine translation. CoRR, 2018. URL: http://arxiv.org/abs/1808.07512, arXiv:1808.07512.
Jason Weston, Sumit Chopra, and Antoine Bordes. Memory networks. CoRR, 2014. URL: http://arxiv.org/abs/1410.3916.
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, and others. Google's neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016.
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. ICML, feb 2015. URL: http://arxiv.org/abs/1502.03044, arXiv:1502.03044.
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. Show, attend and tell: neural image caption generation with visual attention. CoRR, 2015. URL: http://arxiv.org/abs/1502.03044.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. Hierarchical attention networks for document classification. In Proc ACL. 2016.
Biao Zhang, Deyi Xiong, and Jinsong Su. Accelerating neural transformer via an average attention network. CoRR, 2018. URL: http://arxiv.org/abs/1805.00631, arXiv:1805.00631.
G. Neubig. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial. ArXiv e-prints, March 2017. arXiv:1703.01619.