Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study

• URL: http://arxiv.org/abs/2206.02436v1
• Date: Mon, 6 Jun 2022 08:56:32 GMT
• Title: Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study
• Authors: Na Li, John D. Kelleher, Robert Ross
• Abstract summary: This paper studies a user-avatar dialogue scenario to study the manifestation of confusion and in the long term its mitigation. We present a new definition of confusion that is particularly tailored to the requirements of intelligent conversational system development. Three pre-trained deep learning models were deployed to estimate base emotion, head pose and eye gaze.
• Score: 8.452193618860356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To this end, in this paper, we present our initial research centred on a user-avatar dialogue scenario that we have developed to study the manifestation of confusion and in the long term its mitigation. We present a new definition of confusion that is particularly tailored to the requirements of intelligent conversational system development for task-oriented dialogue. We also present the details of our Wizard-of-Oz based data collection scenario wherein users interacted with a conversational avatar and were presented with stimuli that were in some cases designed to invoke a confused state in the user. Post study analysis of this data is also presented. Here, three pre-trained deep learning models were deployed to estimate base emotion, head pose and eye gaze. Despite a small pilot study group, our analysis demonstrates a significant relationship between these indicators and confusion states. We understand this as a useful step forward in the automated analysis of the pragmatics of dialogue.

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