Abstract
This paper describes the system we submitted to Task 5 in NLPCC 2018, i.e., Multi-Turn Dialogue System in Open-Domain. This work focuses on the second subtask: Retrieval Dialogue System. Given conversation sessions and 10 candidates for each dialogue session, this task is to select the most appropriate response from candidates. We design a memory-based matching network integrating sequential matching network and several NLP features together to address this task. Our system finally achieves the precision of 62.61% on test set of NLPCC 2018 subtask 2 and officially released results show that our system ranks 1st among all the participants.
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Acknowledgements
The authors would like to thank the task organizers for their efforts, which makes this event interesting. And the authors would like to thank all reviewers for their helpful suggestions and comments, which improve the final version of this work. This work is supported by the Science and Technology Commission of Shanghai Municipality Grant (No. 15ZR1410700) and the open project of Shanghai Key Laboratory of Trustworthy Computing (No. 07dz22304201604).
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Lu, X., Lan, M., Wu, Y. (2018). Memory-Based Matching Models for Multi-turn Response Selection in Retrieval-Based Chatbots. 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_23
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DOI: https://doi.org/10.1007/978-3-319-99495-6_23
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