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.
CITATION STYLE
Lu, X., Lan, M., & Wu, Y. (2018). Memory-Based Matching Models for Multi-turn Response Selection in Retrieval-Based Chatbots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11108 LNAI, pp. 269–278). Springer Verlag. https://doi.org/10.1007/978-3-319-99495-6_23
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