Memory-Based Matching Models for Multi-turn Response Selection in Retrieval-Based Chatbots

Xingwu Lu, Man Lan, Yuanbin Wu

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.