Break, Repair, Learn, Break Less

Investigating User Preferences for Assignment of Divergent Phrasing Learning Burden in Human-Agent Interaction to Minimize Conversational Breakdowns

Conference paper (2022)

Authors

Mina Foosherian BIBA – Bremer Institut für Produktion und Logistik GmbH
Karl A. Hribernik BIBA – Bremer Institut für Produktion und Logistik GmbH
Klaus-Dieter Thoben University of Bremen
Copyright: © 2022 Mina Foosherian, S. Kernan Freire, E. Niforatos, Karl A. Hribernik, Klaus-Dieter Thoben
DOI
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https://doi.org/10.1145/3568444.3568454
Conversational Agents Learning User Experience Error Handling Human centered AI Conversational Breakdown
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Published Date

2022

Language

English

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Abstract

Conversational agents (CA) occasionally fail to understand the user's intention or respond inappropriately due to natural language complexity. These conversational breakdowns can happen because of low intent and entity prediction confidence scores. A promising repair strategy in such cases is that the CA proposes to users likely alternatives to proceed. If one of these options matches the user's intention, the breakdown is repaired successfully. We propose that successful repairs should be followed by a learning mechanism to minimize future breakdowns. After a successful repair, the CA, user, or both can learn each other's specific phrasing. This prevents similar phrasings from causing reoccurring breakdowns. We compared user preferences for these learning mechanisms in a scenario-based study with manufacturing workers (). Our result showed that users first prefer to share the learning burden with the CA (61.3%), followed by entirely outsourcing the learning burden to the CA (60.7%) as opposed to themselves.