Papers

Machine Translation

• Kyunghyun Cho, Bart Van Merrienboer, C¸ alar G¨ulc¸ehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734, Doha, Qatar, October 2014.
• Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (eds.), Advances in Neural Information Processing Systems 27, pp. 3104–3112. Curran Associates, Inc., 2014.
• YonghuiWu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi,Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. Google’s neural machine translation system: Bridging the gap between human and machine translation. 2016.
• Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.h [Attention]

Neural Conversation

• Oriol Vinyals and Quoc Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015.
• Lifeng Shang, Zhengdong Lu, and Hang Li. Neural responding machine for short-text conversation. arXiv preprint arXiv:1503.02364, 2015.
• Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714, 2015.
• Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055, 2015.
• Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. Deep reinforcement learning for dialogue generation. CoRR, abs/1606.01541, 2016.

Embedding Related

• Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model[J]. Journal of machine learning research, 2003, 3(Feb): 1137-1155.
• Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(Aug): 2493-2537.
• Wang, S. and Manning, C. D. (2012). Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pages 90–94. Association for Computational Linguistics.
• Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022.
• Huang, E. H., Socher, R., Manning, C. D., and Ng, A. Y. (2012). Improving word representations via global context and multiple word prototypes. In ACL, pages 873–882.
• Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., and Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In EMNLP, volume 1631, page 1642.
• Mesnil, G., Mikolov, T., Ranzato, M., and Bengio, Y. (2014). Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint arXiv:1412.5335.
• Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
• Mikolov, T., Yih, W.-t., and Zweig, G. (2013b). Linguistic regularities in continuous space word representations. In HLT-NAACL, volume 13, pages 746–751.
• Mikolov, T. and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems.
• Le, Q. V. and Mikolov, T. (2014). Distributed representations of sentences and documents. In ICML, volume 14, pages 1188–1196.
• Dai, A. M., Olah, C., and Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998.
• Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., and Fidler, S. (2015). Skip-thought vectors. In Advances in neural information processing systems, pages 3294–3302.
• Zhang, X. and LeCun, Y. (2015). Text understanding from scratch. arXiv preprint arXiv:1502.01710.
• Tai, K. S., Socher, R., and Manning, C. D. (2015). Improved semantic representations from treestructured long short-term memory networks. arXiv preprint arXiv:1503.00075.
• Dai, A. M. and Le, Q. V. (2015). Semi-supervised sequence learning. In Advances in Neural Information Processing Systems, pages 3079–3087.
• Kusner, M. J., Sun, Y., Kolkin, N. I., and Weinberger, K. Q. (2015). From word embeddings to document distances. In Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pages 957–966.
• Chen M. Efficient Vector Representation for Documents through Corruption[J]. arXiv preprint arXiv:1707.02377, 2017.

Memory-based Models

• Weston J, Chopra S, Bordes A. Memory networks[J]. arXiv preprint arXiv:1410.3916, 2014.
• Sukhbaatar S, Weston J, Fergus R. End-to-end memory networks[C]//Advances in neural information processing systems. 2015: 2440-2448.
• Kumar A, Irsoy O, Ondruska P, et al. Ask me anything: Dynamic memory networks for natural language processing[C]//International Conference on Machine Learning. 2016: 1378-1387.
• Graves, Alex, Wayne, Greg, and Danihelka, Ivo. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014b.
• Joulin, Armand and Mikolov, Tomas. Inferring algorithmic patterns with stack-augmented recurrent nets. arXiv preprint arXiv:1503.01007, 2015.
• Grefenstette, Edward, Hermann, Karl Moritz, Suleyman, Mustafa, and Blunsom, Phil. Learning to transduce with unbounded memory. arXiv preprint arXiv:1506.02516, 2015.

Language Model

• Mikolov, T., Karafi´at, M., Burget, L., Cernocky, J., and Khudanpur, S. (2010). Recurrent neural network based language model. In Interspeech, volume 2, page 3.
• Kim, Y., Jernite, Y., Sontag, D., and Rush, A. M. (2015). Character-aware neural language models. arXiv preprint arXiv:1508.06615.

Unsupervised Learning

• G. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504 { 507, 2006.
• Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096–1103. ACM.
• R. Salakhutdinov and G. Hinton. Deep Boltzmann machines. In Proceedings of the International Conference on Articial Intelligence and Statistics, volume 5, pages 448{455, 2009.
• P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. In Proceedings of the 27th International Conference on Machine Learning, pages 3371{3408. ACM, 2010.
• Chen, M., Xu, Z., Weinberger, K., and Sha, F. (2012). Marginalized denoising autoencoders for domain adaptation. arXiv preprint arXiv:1206.4683.

Misc

• Srivastava N, Hinton G E, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

分词

• Wenzhe Pei,Tao Ge, Baobao Chang.Max-Margin Tensor Neural Network for ChineseWord Segmentation.Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 293–303, Baltimore, Maryland, USA, June 23-25 2014.
• Jianqiang Ma,Erhard Hinrichs. Accurate Linear-Time Chinese Word Segmentation via Embedding Matching. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1733–1743, Beijing, China, July 26-31, 2015.
• Xinchi Chen, Xipeng Qiu, Chenxi Zhu, Xuanjing Huang.Gated Recursive Neural Network for Chinese Word Segmentation. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1744–1753,Beijing, China, July 26-31, 2015.

Visual Attention

• Mnih, Volodymyr, Heess, Nicolas, Graves, Alex, et al. Recurrent models of visual attention. In Advances in Neural Information Processing Systems, pp. 2204–2212, 2014.
• Ba, Jimmy, Mnih, Volodymyr, and Kavukcuoglu, Koray. Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755, 2014.

Turing Complete

• Schmidhuber, Juergen. Self-delimiting neural networks. arXiv preprint arXiv:1210.0118, 2012.
• Schmidhuber, Juergen. Optimal ordered problem solver. Machine Learning, 54(3):211–254, 2004.