When there is no progress with a task-oriented chatbot: A conversation analysis

Chi Hsun Li, Ken Chen, Yung-Ju Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Task-oriented chatbots are increasingly prevalent in our daily life. Research effort has been devoted to advancing our understanding of users' interaction with conversational agents, including conversation breakdowns. However, most research attempts were limited to obversions from a relatively short duration of user interaction with chatbots, where users were aware of being studied. In this study, we conducted a conversation analysis on a three-month conversation log of users conversing with a chatbot of a banking institution. The log consisted of 1,837 users' conversations with this chatbot with 19,449 message exchanges. From this analysis, we show that users more often failed to make a progress in a conversation when they requested information than when they provided information. Furthermore, we uncovered five kinds of intention gaps unexpected to the chatbot, and five major behaviors users adopted to cope with non-progress.

Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450368254
DOIs
StatePublished - 1 Oct 2019
Event21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019 - Taipei, Taiwan
Duration: 1 Oct 20194 Oct 2019

Publication series

NameProceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019

Conference

Conference21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019
CountryTaiwan
CityTaipei
Period1/10/194/10/19

Keywords

  • Chatbot
  • Coping strategy
  • Human-agent interaction
  • Interaction gaps

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