Generating sentences from a continuous space

CoNLL 2016 - 20th SIGNLL Conference on Computational Natural Language Learning, Proceedings

Volumn , Issue , 2016, Pages 10-21

  Bowman, Samuel R ^a   Vilnis, Luke ^b   Vinyals, Oriol ^c   Dai, Andrew M ^c   Jozefowicz, Rafal ^c   Bengio, Samy ^c  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF MASSACHUSETTS   (United States)

^c GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; MODELING LANGUAGES; NATURAL LANGUAGE PROCESSING SYSTEMS; RECURRENT NEURAL NETWORKS;

AUTO ENCODERS; CONTINUOUS SPACES; GENERATIVE MODEL; LANGUAGE MODEL; LEARNING PROBLEM; SYNTACTIC FEATURES;

LEARNING SYSTEMS;

EID: 85072753030     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/k16-1002     Document Type: Conference Paper

References (39)

1. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proc. ICLR.
2. Justin Bayer and Christian Osendorfer. 2015. Learning stochastic recurrent networks. arXiv preprint arXiv:1411.7610.
3. Julian Besag. 1986. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society Series B (Methodological) pages 48–259.
4. Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, and Yoshua Bengio. 2015. A recurrent latent variable model for sequential data. In Proc. NIPS.
5. Andrew M. Dai and Quoc V. Le. 2015. Semi-supervised sequence learning. In Proc. NIPS.
6. Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In Proc. NIPS.
7. Bill Dolan, Chris Quirk, and Chris Brockett. 2004. Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In Proc. COLING.
8. Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proc. CVPR.
9. Otto Fabius and Joost R. van Amersfoort. 2014. Variational recurrent auto-encoders. arXiv preprint arXiv:1412.6581.
10. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proc. NIPS.
11. Karol Gregor, Ivo Danihelka, Alex Graves, and Daan Wierstra. 2015. DRAW: A recurrent neural network for image generation. In Proc. ICML.
12. Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012. A kernel two-sample test. JMLR 13(1):723–773.
13. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9(8).
14. Mohit Iyyer, Jordan Boyd-Graber, and Hal Daumé III. 2014. Generating sentences from semantic vector space representations. In Proc. NIPS Workshop on Learning Semantics.
15. Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015. Deep unordered composition rivals syntactic methods for text classification. In Proc. ACL.
16. Michael Kearns, Yishay Mansour, and Andrew Y Ng. 1998. An information-theoretic analysis of hard and soft assignment methods for clustering. In Learning in graphical models, Springer, pages 495–520.
17. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proc. EMNLP.
18. Diederik P. Kingma and Max Welling. 2015. Auto-encoding variational bayes. In Proc. ICLR.
19. Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S Zemel, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2015. Skip-thought vectors. In Proc. NIPS.
20. Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher. 2016. Ask me anything: Dynamic memory networks for natural language processing. In Proc. ICML.
21. Quoc V. Le and Tomáš Mikolov. 2014. Distributed representations of sentences and documents. In Proc. ICML.
22. Jiwei Li, Minh-Thang Luong, and Dan Jurafsky. 2015a. A hierarchical neural autoencoder for paragraphs and documents. In Proc. ACL.
23. Xin Li and Dan Roth. 2002. Learning question classifiers. In Proc. COLING.
24. Yujia Li, Kevin Swersky, and Richard Zemel. 2015b. Generative moment matching networks. In Proc. ICML.
25. Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan Yuille. 2015. Deep captioning with multimodal recurrent neural networks (m-RNN). In Proc. ICLR.
26. Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational linguistics 19(2):313–330.
27. Yishu Miao, Lei Yu, and Phil Blunsom. 2016. Neural variational inference for text processing. In Proc. ICML.
28. Tomáš Mikolov, Stefan Kombrink, Lukáš Burget, Jan Honza Černockỳ, and Sanjeev Khudanpur. 2011. Extensions of recurrent neural network language model. In Proc. ICASSP.
29. Tapani Raiko, Mathias Berglund, Guillaume Alain, and Laurent Dinh. 2015. Techniques for learning binary stochastic feedforward neural networks. In Proc. ICLR.
30. Danilo J. Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. In Proc. ICML.
31. Danilo J. Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proc. ICML.
32. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical Bayesian optimization of machine learning algorithms. In Proc. NIPS.
33. Richard Socher, Eric H Huang, Jeffrey Pennin, Christopher D. Manning, and Andrew Y. Ng. 2011. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In Proc. NIPS.
34. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. JMLR 15(1):1929–1958.
35. Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015. Training very deep networks. In Proc. NIPS.
36. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proc. NIPS.
37. Lucas Theis and Matthias Bethge. 2015. Generative image modeling using spatial LSTMs. In Proc. NIPS.
38. Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2015. Show and tell: A neural image caption generator. In Proc. CVPR.
39. Han Zhao, Zhengdong Lu, and Pascal Poupart. 2015. Self-adaptive hierarchical sentence model. In Proc. IJCAI.