Abstract
This paper describes our method for sub-task 2 of Task 5: multi-turn conversation retrieval, in NLPCC2018. Given a context and some candidate responses, the task is to choose the most reasonable response for the context. It can be regarded as a matching problem. To address this task, we propose a deep neural model named RCMN which focus on modeling relevance consistency of conversations. In addition, we adopt one existing deep learning model which is advanced for multi-turn response selection. And we propose an ensemble strategy for the two models. Experiments show that RCMN has good performance, and ensemble of two models makes good improvement. The official results show that our solution takes 2nd place. We open the source of our code on GitHub, so that other researchers can reproduce easily.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv (2014)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. Adv. Neural Inf. Process. Syst. 3, 2042–2050 (2015)
Huang, P.S., He, X., Gao, J., Deng, L., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: ACM International Conference on Conference on Information & Knowledge Management, pp. 2333–2338 (2013)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)
Lecun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (2014)
Lowe, R., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. Computer Science (2015)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)
Rumerlhar, D.E.: Learning representation by back-propagating errors. Nature 323(3), 533–536 (1986)
Wan, S., Lan, Y., Guo, J., Xu, J., Pang, L., Cheng, X.: A deep architecture for semantic matching with multiple positional sentence representations, pp. 2835–2841 (2015)
Wan, S., Lan, Y., Xu, J., Guo, J., Pang, L., Cheng, X.: Match-SRNN: modeling the recursive matching structure with spatial RNN. Comput. Graph. 28(5), 731–745 (2016)
Wu, Y., et al.: Sequential matching network: a new architecture for multi-turn response selection in retrieval-based chatbots. In: Meeting of the Association for Computational Linguistics, pp. 496–505 (2017)
Xiong, C., Dai, Z., Callan, J., Power, R., Power, R.: End-to-end neural ad-hoc ranking with kernel pooling, pp. 55–64 (2017)
Yang, L., Ai, Q., Guo, J., Croft, W.B.: aNMM: ranking short answer texts with attention-based neural matching model. In: ACM International on Conference on Information and Knowledge Management, pp. 287–296 (2016)
Zhou, X., et al.: Multi-view response selection for human-computer conversation. In: Conference on Empirical Methods in Natural Language Processing, pp. 372–381 (2016)
Acknowledgments
This work was supported in part by the Natural Science Foundation of China (Grand Nos. U61672081, 1636211, 61370126), and Beijing Advanced Innovation Center for Imaging Technology (No. BAICIT-2016001) and National Key R&D Program of China (No. 2016QY04W0802).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Yan, Z., Li, Z., Chao, W. (2018). Response Selection of Multi-turn Conversation with Deep Neural Networks. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-99495-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99494-9
Online ISBN: 978-3-319-99495-6
eBook Packages: Computer ScienceComputer Science (R0)
