Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

ABSTRACT

To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Along with the prosperity of Web 2.0, we are now able to collect extremely massive conversational data, which are publicly available. It casts a great opportunity to launch automatic conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will be able to find at least some responses from the massive repository for any user inputs. Given a human issued message, i.e., query, our system would provide a reply after adequate training and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data. The proposed model is general and unified for different conversation scenarios in open domain. We incorporate the impact of multiple data inputs, and formulate various features and factors with optimization into the deep learning framework. In the experiments, we investigate the effectiveness of the proposed deep neural network structures with better combinations of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of p@1, MAP, nDCG, and MRR for conversational purposes.

References

1. Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1):1--127, 2009.
2. F. Bessho, T. Harada, and Y. Kuniyoshi. Dialog system using real-time crowdsourcing and Twitter large-scale corpus. In SIGDIAL, pages 227--231, 2012.
3. G. Cong, L. Wang, C.-Y. Lin, Y.-I. Song, and Y. Sun. Finding question-answer pairs from online forums. In SIGIR, pages 467--474.
4. A. Graves, A.-r. Mohamed, and G. Hinton. Speech recognition with deep recurrent neural networks. In Proc. Acoustics, Speech and Signal Processing, pages 6645--6649, 2013.
5. H. He, K. Gimpel, and J. Lin. Multi-perspective sentence similarity modeling with convolutional neural networks. In EMNLP, pages 1576--1586, 2015.
6. R. Higashinaka, K. Imamura, T. Meguro, C. Miyazaki, N. Kobayashi, H. Sugiyama, T. Hirano, T. Makino, and Y. Matsuo. Towards an open domain conversational system fully based on natural language processing. In COLING, 2014.
7. B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neural network architectures for matching natural language sentences. In NIPS, pages 2042--2050, 2014.
8. K. Järvelin and J. Kek\"al\"ainen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20(4):422--446, 2002.
9. Z. Ji, Z. Lu, and H. Li. An information retrieval approach to short text conversation. CoRR, abs/1408.6988, 2014.
10. N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188, 2014.
11. C.-J. Lee, Q. Ai, W. B. Croft, and D. Sheldon. An optimization framework for merging multiple result lists. In CIKM '15, pages 303--312, 2015.
12. A. Leuski, R. Patel, D. Traum, and B. Kennedy. Building effective question answering characters. In SIGDIAL, pages 18--27, 2009.
13. A. Leuski and D. Traum. NPCEditor: Creating virtual human dialogue using information retrieval techniques. AI Magazine, 32(2):42--56, 2011.
14. H. Li and J. Xu. Semantic matching in search. Foundations and Trends in Information Retrieval, 8:89, 2014.
15. J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055, 2015.
16. X. Li, L. Mou, R. Yan, and M. Zhang. Stalematebreaker: A proactive content-introducing approach to automatic human-computer conversation. In IJCAI, 2016.
17. Z. Lu and H. Li. A deep architecture for matching short texts. In NIPS, pages 1367--1375, 2013.
18. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.
19. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv:1301.3781, 2013.
20. L. Mou, G. Li, L. Zhang, T. Wang, and Z. Jin. Convolutional neural networks over tree structures for programming language processing. In AAAI, pages 1287--1292, 2016.
21. L. Mou, H. Peng, G. Li, Y. Xu, L. Zhang, and Z. Jin. Discriminative neural sentence modeling by tree-based convolution. In EMNLP, pages 2315--2325, 2015.
22. L. Mou, M. Rui, G. Li, Y. Xu, L. Zhang, R. Yan, and Z. Jin. Recognizing entailment and contradiction by tree-based convolution. arXiv preprint arXiv:1512.08422, 2015.
23. M. Nakano, N. Miyazaki, N. Yasuda, A. Sugiyama, J.-i. Hirasawa, K. Dohsaka, and K. Aikawa. WIT: A toolkit for building robust and real-time spoken dialogue systems. In SIGDIAL, pages 150--159.
24. E. Nouri, R. Artstein, A. Leuski, and D. R. Traum. Augmenting conversational characters with generated question-answer pairs. In AAAI Fall Symposium: Question Generation, 2011.
25. H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward. Deep sentence embedding using the long short term memory network: Analysis and application to information retrieval. arXiv preprint arXiv:1502.06922, 2015.
26. A. Ritter, C. Cherry, and W. B. Dolan. Data-driven response generation in social media. In EMNLP, pages 583--593, 2011.
27. T. Rocktäschel, E. Grefenstette, K. M. Hermann, T. Kočiskỳ, and P. Blunsom. Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664, 2015.
28. A. Severyn and A. Moschitti. Learning to rank short text pairs with convolutional deep neural networks. In SIGIR '15, pages 373--382.
29. L. Shang, Z. Lu, and H. Li. Neural responding machine for short-text conversation. In ACL-IJCNLP, pages 1577--1586, 2015.
30. R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning. Semi-supervised recursive autoencoders for predicting sentiment distributions. In EMNLP, pages 151--161, 2011.
31. H. Sugiyama, T. Meguro, R. Higashinaka, and Y. Minami. Open-domain utterance generation for conversational dialogue systems using Web-scale dependency structures. In SIGDIAL, pages 334--338, 2013.
32. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, pages 3104--3112, 2014.
33. M. A. Walker, R. Passonneau, and J. E. Boland. Quantitative and qualitative evaluation of darpa communicator spoken dialogue systems. In ACL, pages 515--522, 2001.
34. R. S. Wallace. The Anatomy of ALICE. Springer, 2009.
35. H. Wang, Z. Lu, H. Li, and E. Chen. A dataset for research on short-text conversations. In EMNLP, pages 935--945, 2013.
36. J. Williams, A. Raux, D. Ramachandran, and A. Black. The dialog state tracking challenge. In SIGDIAL, pages 404--413, 2013.
37. Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen, Y. Lu, and Z. Jin. Improved relation classification by deep recurrent neural networks with data augmentation. arXiv preprint arXiv:1601.03651, 2016.
38. Y. Xu, L. Mou, G. Li, Y. Chen, H. Peng, and Z. Jin. Classifying relations via long short term memory networks along shortest dependency paths. In EMNLP, 2015.
39. R. Yan. i, poet: Automatic poetry composition through recurrent neural networks with iterative polishing schema. In IJCAI, 2016.
40. R. Yan, M. Lapata, and X. Li. Tweet recommendation with graph co-ranking. In ACL, pages 516--525, 2012.
41. R. Yan, C.-T. Li, H.-P. Hsieh, P. Hu, X. Hu, and T. He. Socialized language model smoothing via bi-directional influence propagation on social networks. In WWW '16, pages 1395--1405, 2016.
42. R. Yan, X. Wan, J. Otterbacher, L. Kong, X. Li, and Y. Zhang. Evolutionary timeline summarization: A balanced optimization framework via iterative substitution. In SIGIR '11, pages 745--754, 2011.
43. R. Yan, I. E. Yen, C.-T. Li, S. Zhao, and X. Hu. Tackling the achilles heel of social networks: Influence propagation based language model smoothing. In WWW '15, pages 1318--1328, 2015.
44. K. Zhai and D. J. Williams. Discovering latent structure in task-oriented dialogues. In ACL, pages 36--46, 2014.
45. B. Zhang, J. Su, D. Xiong, Y. Lu, H. Duan, and J. Yao. Shallow convolutional neural network for implicit discourse relation recognition. In EMNLP, pages 2230--2235, 2015.

Index Terms

• Information systems

  • Information retrieval

    • Document representation

    • Retrieval models and ranking

      • Learning to rank

  • World Wide Web

    • Web applications

      • Internet communications tools

        • Chat