User intent prediction in information-seeking conversations

Chen Qu, Yongfeng Zhang, Liu Yang, Johanne R. Trippas, W. Bruce Croft, Minghui Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.

Original languageEnglish (US)
Title of host publicationCHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages25-33
Number of pages9
ISBN (Electronic)9781450360258
DOIs
StatePublished - Mar 8 2019
Event4th ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2019 - Glasgow, United Kingdom
Duration: Mar 10 2019Mar 14 2019

Publication series

NameCHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval

Conference

Conference4th ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2019
Country/TerritoryUnited Kingdom
CityGlasgow
Period3/10/193/14/19

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Information Systems

Keywords

  • Conversational search
  • Information-seeking conversations
  • Multi-turn question answering
  • User intent prediction

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