Author ORCID Identifier
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Computer Sciences, Communication engineering and systems
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 see this as a useful step forward in the automated analysis of the pragmatics of dialogue.
Li, N., Ross, R. & Kelleher, J.D. (2021). Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study. 25th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2021), University of Potsdam, Germany // The Internet, June 7. doi:10.21427/bsd0-7326
Science Foundation Ireland