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Adaptive Dialogue Management for Conversational Information Elicitation

Published: 07 July 2022 Publication History

Abstract

Information elicitation conversations, for example, when a medical professional asks about a patient's history or a sales agent tries to understand their client's preferences, often start with a set of routine questions. The interviewer asks a predetermined set of questions conversationally, adapting them to the unique characteristics and context of an individual. Multiple-choice questionnaires are commonly used as a screening tool before the client sees the professional for more efficient information elicitation [5]. However, recent proof-of-concept studies show that users are more likely to report their symptoms to an embodied conversational agent (ECA) than on a pen-and-paper survey [3], and rate ECAs highly on user experience [4]. Chatbots allow the user to give free-form responses and ask clarification questions instead of having to interpret and choose from a list of given options. They can also keep the user engaged by sharing relevant information and offering empathetic acknowledgments when appropriate. However, many of the technical challenges involved in building such a conversational agent remain unsolved.

References

[1]
Zahra Ashktorab, Mohit Jain, Q Vera Liao, and Justin D Weisz. 2019. Resilient chatbots: Repair strategy preferences for conversational breakdowns. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1--12.
[2]
Bingjie Liu and S Shyam Sundar. 2018. Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychology, Behavior, and Social Networking, Vol. 21, 10 (2018), 625--636.
[3]
Gale M Lucas, Albert Rizzo, Jonathan Gratch, Stefan Scherer, Giota Stratou, Jill Boberg, and Louis-Philippe Morency. 2017. Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Frontiers in Robotics and AI, Vol. 4 (2017), 51.
[4]
Pierre Philip, Jean-Arthur Micoulaud-Franchi, Patricia Sagaspe, Etienne De Sevin, Jérôme Olive, Stéphanie Bioulac, and Alain Sauteraud. 2017. Virtual human as a new diagnostic tool, a proof of concept study in the field of major depressive disorders. Scientific reports, Vol. 7, 1 (2017), 1--7.
[5]
Robert L Spitzer, Kurt Kroenke, Janet BW Williams, and Bernd Löwe. 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of internal medicine, Vol. 166, 10 (2006), 1092--1097.

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  1. Adaptive Dialogue Management for Conversational Information Elicitation

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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 07 July 2022

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    Author Tags

    1. dialogue management
    2. goal-oriented dialogue systems
    3. intent recognition

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