Response Selection of Multi-turn Conversation with Deep Neural Networks

摘要：

　　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 multiturn 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.

关键词： Multi-turn conversation Response selection Relevance consistency

作者: Yunli Wang Zhao Yan Zhoujun Li Wenhan Chao

作者单位: Beihang University,Beijing,China Tencent,Beijing,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 110-119

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）