CASIA OpenIR  > 复杂系统认知与决策实验室
Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks
Shizhu HE1; Kang Liu1,2; Weiting An3
2019-01-27
Conference NameThe Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Conference Date2019.01.27 - 2019.02.01
Conference PlaceHonolulu, Hawaii, USA
Abstract

Customers ask questions, and customer service staffs answer those questions. It is the basic service manner of customer service (CS). The progress of CS is a typical multi-round con- versation. However, there are no explicit corresponding rela- tions among conversational utterances. This paper focuses on obtaining explicit alignments of question and answer utter- ances in CS. It not only is an important task of dialogue analy- sis, but also able to obtain lots of valuable train data for learn- ing dialogue systems. In this work, we propose end-to-end models for aligning question (Q) and answer (A) utterances in CS conversation with recurrent pointer networks (RPN). On the one hand, RPN-based alignment models are able to model the conversational contexts and the mutual influence of different Q-A alignments. On the other hand, they are able to address the issue of empty and multiple alignments for some utterances in a unified manner. We construct a dataset from an in-house online CS. The experimental results demonstrate that the proposed models are effective to learn the alignments of question and answer utterances.

Language英语
Sub direction classification自然语言处理
planning direction of the national heavy laboratory语音语言处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57451
Collection复杂系统认知与决策实验室
Affiliation1.Institute of Automation, Chinese Academy of Science
2.University of Chinese Academy of Sciences
3.Alibaba Group
Recommended Citation
GB/T 7714
Shizhu HE,Kang Liu,Weiting An. Learning to Align Question and Answer Utterances in Customer Service Conversation with Recurrent Pointer Networks[C],2019.
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