Abstract
As an important step of human-computer interaction, conversion generation has attracted much attention and has a rising tendency in recent years. This paper gives a detailed description about an ensemble system for short text conversation generation. The proposed system consists of four subsystems, a quick response candidates selecting module, an information retrieval system, a generation-based system and an ensemble module. An advantage of this system is that multiple versions of generated responses are taken into account resulting a more reliable output. In the NLPCC 2017 shared task “Emotional Conversation Generation Challenge”, the ensemble system generates appropriate responses for Chinese SNS posts and ranks at the top of participant list.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
References
Sikorski, T., Allen, J.F.: A task-based evaluation of the TRAINS-95 dialogue system. In: Maier, E., Mast, M., LuperFoy, S. (eds.) DPSLS 1996. LNCS, vol. 1236, pp. 207–220. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63175-5_48
Litman, D.J., Silliman, S.: ITSPOKE: an intelligent tutoring spoken dialogue system. In: Demonstration Papers at HLT-NAACL 2004. Association for Computational Linguistics (2004)
Clancey, W.J.: Tutoring rules for guiding a case method dialogue. Int. J. Man Mach. Stud. 11(1), 25–49 (1979)
Levin, E., Pieraccini, R., Eckert, W.: Using Markov decision process for learning dialogue strategies. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1. IEEE (1998)
Huang, C., et al.: LODESTAR: a mandarin spoken dialogue system for travel information retrieval. In: Sixth European Conference on Speech Communication and Technology (1999)
Eric, M., Manning, C.D.: Key-value retrieval networks for task-oriented dialogue. arXiv preprint arXiv:1705.05414 (2017)
Serban, I.V., et al.: Building end-to-end dialogue systems using generative hierarchical neural network models. In: AAAI (2016)
Li, J., et al.: Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016)
Song, Y., et al.: Two are better than one: an ensemble of retrieval-and generation-based dialog systems. arXiv preprint arXiv:1610.07149 (2016)
Zhou, H., Huang, M., Zhu, X., Liu, B.: Emotional chatting machine: emotional conversation generation with internal and external memory. arXiv:1704.01074 (2017)
Lin, Z., et al.: A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017)
Vijayakumar, A.K., Cogswell, M., Selvaraju, R.R., et al.: Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models (2016)
Ji, Z., Lu, Z., Li, H.: An information retrieval approach to short text conversation. arXiv:1408.6988 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Zhuang, Y., Wang, X., Zhang, H., Xie, J., Zhu, X. (2018). An Ensemble Approach to Conversation Generation. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-73618-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73617-4
Online ISBN: 978-3-319-73618-1
eBook Packages: Computer ScienceComputer Science (R0)