Abstract
Existing task-oriented dialogue systems seldom emphasize multi-intent scenarios, which makes them hard to track complex intent switch in a multi-turn dialogue, and even harder to make proactive reactions for the user’s next potential intent. In this paper, we formalize the multi-intent tracking task and introduce a complete set of intent switch modes. Then we propose ISwitch, a system that can handle complex multi-intent dialogue interactions. In this system, we design a gated controller to recognize the current intent, and a proactive mechanism to predict the next potential intent. Based on these, we use pre-defined patterns to generate proper responses. Experiments show that our model can achieve high intent recognition accuracy, and simplify the dialogue process. We also construct and release a new dataset for complex multi-turn multi-intent-switch dialogue.
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References
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)
Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neuroner: an easy-to-use program for named-entity recognition based on neural networks. In: EMNLP (2017)
El Asri, L., et al.: Frames: a corpus for adding memory to goal-oriented dialogue systems. In: SIGdial, pp. 207–219 (2017)
Eric, M., Krishnan, L., Charette, F., Manning, C.D.: Key-value retrieval networks for task-oriented dialogue. In: SIGdial, pp. 37–49 (2017)
Hemphill, C.T., Godfrey, J.J., Doddington, G.R.: The ATIS spoken language systems pilot corpus. In: Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, 24–27 June 1990 (1990)
Henderson, M., Thomson, B., Williams, J.: Dialog state tracking challenge 2 and 3 (2013)
Henderson, M., Thomson, B., Williams, J.D.: The second dialog state tracking challenge. In: SIGDIAL, pp. 263–272 (2014)
Henderson, M., Thomson, B., Young, S.: Word-based dialog state tracking with recurrent neural networks. In: SIGDIAL (2014)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Lee, K., et al.: An assessment framework for dialport. In: Proceedings of the International Workshop on Spoken Dialogue Systems Technology (2017)
Lee, S., Eskenazi, M.: Recipe for building robust spoken dialog state trackers: dialog state tracking challenge system description. In: SIGDIAL (2013)
Lemon, O., Bracy, A., Gruenstein, A., Peters, S.: The WITAS multi-modal dialogue system I. In: Seventh European Conference on Speech Communication and Technology (2001)
Mrkšić, N., Séaghdha, D.Ó., Wen, T.H., Thomson, B., Young, S.: Neural belief tracker: data-driven dialogue state tracking. In: ACL, vol. 1, pp. 1777–1788 (2017)
Nakano, M., Sato, S., Komatani, K., Matsuyama, K., Funakoshi, K., Okuno, H.G.: A two-stage domain selection framework for extensible multi-domain spoken dialogue systems. In: SIGDIAL (2011)
Price, P.J.: Evaluation of spoken language systems: the ATIS domain. In: Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, 24–27 June 1990 (1990)
Rayner, M., Lewin, I., Gorrell, G., Boye, J.: Plug and play speech understanding. In: SIGdial Workshop (2001)
Serban, I.V., Sordoni, A., Bengio, Y., Courville, A., Pineau, J.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: AAAI, pp. 3776–3783 (2016)
Sun, K., Chen, L., Zhu, S., Yu, K.: The SJTU system for dialog state tracking challenge 2. In: SIGDIAL (2014)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS (2014)
Thomson, B., Young, S.: Bayesian update of dialogue state: a POMDP framework for spoken dialogue systems. Comput. Speech Lang. (2010)
Ultes, S., et al.: PyDial: a multi-domain statistical dialogue system toolkit. In: Proceedings of ACL 2017, System Demonstrations
Wen, T.H., et al.: A network-based end-to-end trainable task-oriented dialogue system. In: EACL, vol. 1, pp. 438–449 (2017)
Williams, J., Raux, A., Ramachandran, D., Black, A.: The dialog state tracking challenge. In: SIGDIAL, pp. 404–413 (2013)
Williams, J.D.: Web-style ranking and SLU combination for dialog state tracking. In: SIGDIAL (2014)
Williams, J.D., Asadi, K., Zweig, G.: Hybrid code networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. In: ACL, vol. 1, pp. 665–677 (2017)
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Our work is supported by the National Key Research and Development Program of China under Grant No. 2017YFB1002101 and National Natural Science Foundation of China under GrantNo. 61433015.
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Shi, C. et al. (2019). We Know What You Will Ask: A Dialogue System for Multi-intent Switch and Prediction. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_8
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DOI: https://doi.org/10.1007/978-3-030-32233-5_8
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