Semi-supervised learning for information extraction from dialogue

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn 2018-September, Issue , 2018, Pages 2077-2081

  Kannan, Anjuli ^a   Chen, Kai ^a   Jaunzeikare, Diana ^a   Rajkomar, Alvin ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RECURRENT NEURAL NETWORKS; SEMANTICS; SIGNAL ENCODING; SPEECH COMMUNICATION;

CLASSIFICATION TASKS; CLINICAL DOCUMENTATION; FEATURE REPRESENTATION; HUMAN EVALUATION; RECURRENT NEURAL NETWORK (RNN); SEMANTIC INFORMATION; SEMI- SUPERVISED LEARNING; SUPERVISED LEARNING PROBLEMS;

SUPERVISED LEARNING;

EID: 85054954639     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: 10.21437/Interspeech.2018-1318     Document Type: Conference Paper

References (25)

1. A. Verghese, N. Shah, and R. Harrington, “What this computer needs is a physician: Humanism and artificial intelligence,” JAMA, Dec. 2017.
2. C. C. Chiu, A. Tripathi, K. Chou, C. Co, N. Jaitly, D. Jaunzeikare, A. Kannan, P. Nguyen, H. Sak, A. Sankar, J. Tansuwan, and N. Wan, “Speech recognition for medical conversations,” CoRR, vol. abs/1711.07274, 2017.
3. A. M. Dai and Q. V. Le, “Semi-supervised sequence learning.,” NIPS, 2015.
4. P. Ramachandran, P. J. Liu, and Q. V. Le, “Unsupervised pretrain-ing for sequence to sequence learning,” CoRR, vol. abs/1611.02683, 2016.
5. A. Radford, R. Jozefowicz, and I. Sutskever, “Learning to generate reviews and discovering sentiment,” CoRR, vol. abs/1704.01444, 2017.
6. R. Kiros, Y. Zhu, R. Salakhutdinov, R. Zemel, A. Torralba, R. Ur-tasun, and S. Fidler, “Skip-thought vectors,” in Proceedings of NIPS, 2015.
7. M. T. Luong, Q. V. Le, I. Sutskever, O. Vinyals, and L. Kaiser, “Multi-task sequence to sequence learning.,” in Proceedings of ICLR, 2015.
8. B. Zoph, D. Yuret, J. May, and K. Knight, “Transfer learning for low-resource neural machine translation.,” in Proceedings of EMNLP, 2016.
9. O. Firat, B. Sankaran, Y. Al-Onaizan, F. Yarman-Vural, and K. Cho, “Zero-resource translation with multilingual neural machine translation.,” in Proceedings of EMNLP, 2016.
10. D. He, Y. Zia, T. Qin, L. Wang, N. Yu, T. Liu, and W. Ma, “Dual learning for machine translation.,” in Proceedings of NIPS, 2016.
11. Y. Cheng, W. Xu, Z. He, W. He, H. Wu, M. Sun, and Y. Liu, “Semi-supervised learning for neural machine translation.,” in Proceedings of ICLR, 2018.
12. M. Artetxe, G. Labaka, E. Agirre, and K. Cho, “Unsupervised neural machine translation,” in Proceedings of ICLR, 2018.
13. G. Mesnil,, X. He, L. Deng, and Y. Bengio, “Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding,” Interspeech, 2013.
14. G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng, Hakkani-Tur D, X. He, L. Heck, G. Tur, D. Yu, and G. Zweig, “Using recurrent neural networks for slot filling in spoken language understanding,” IEEE/ACM Trans. on Audio, Speech, and Language Processing, 2015.
15. K. Yao, G. Zweig, M-Y. Hwang, Y. Shi, and D. Yu, “Recurrent neural networks for language understanding in interspeech,” Interspeech, 2013.
16. K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi, “Spoken language understanding using long short-term memory neural networks,” IEEE SLT, 2014.
17. A. Bayer, E. Stepanov, and G. Riccardi, “Towards end-to-end spoken dialogue systems with turn embeddings,” in Proc. Interspeech 2017, 2017, pp. 2516-2520.
18. I. Sutskever, O. Vinyals, and Q. Le, “Sequence to sequence learning with neural networks,” Advances in Neural Information Processing Systems (NIPS), 2014.
19. K. Cho, B. van Merriënboer, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proc. EMNLP, 2014.
20. O. Vinyals and Q. Le, “A neural conversation model,” in ICML Deep Learning Workshop, 2015.
21. A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J. Nie, J. Gao, and B. Dolan, “A neural network approach to context-sensitive generation of conversation responses,” in Proceedings of NAACL-HLT, 2015.
22. L. Shang, Z. Lu, and H. Li, “Neural responding machine for short-text conversation,” in Proceedings of ACL-IJCNLP, 2015.
23. I. V. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau, “Hierarchical neural network generative models for movie dialogues,” in arXiv preprint arXiv:1507.04808, 2015.
24. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proceedings of ICLR Workshop, 2013.
25. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.