Enhancing Multi-turn Dialogue Modeling with Intent Information for E-Commerce Customer Service

摘要：

　　Nowadays,it is a heated topic for many industries to build intelligent conversational bots for customer service.A critical solution to these dialogue systems is to understand the diverse and changing intents of customers accurately.However,few studies have focused on the intent information due to the lack of large-scale dialogue corpus with intent labelled.In this paper,we propose to leverage intent information to enhance multi-turn dialogue modeling.First,we construct a large-scale Chinese multi-turn E-commerce conversation corpus with intent labelled,namely E-IntentConv,which covers 289 fine-grained intents in after-sales domain.Specifically,we utilize the attention mechanism to extract Intent Description Words(IDW)for representing each intent explicitly.Then,based on E-IntentConv,we propose to integrate intent information for both retrieval-based model and generation-based model to verify its effectiveness for multi-turn dialogue modeling.Experimental results show that extra intent information is useful for improving both response selection and generation tasks.

关键词： Multi-turn dialogue modeling Large-scale dialogue corpus Intent information

作者: Ruixue Liu Meng Chen Hang Liu Lei Shen Yang Song Xiaodong He

作者单位: JD AI,Beijing,China Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Acade

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 65-77

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）