Abstract
This research aims at defining a real-time probabilistic model of user’s engagement in advice-giving dialogues. We propose an approach based on Hidden Markov Models (HMMs) to describe the differences in the dialogue pattern due to the different level of engagement experienced by the users. We train our HMM models on a corpus of natural dialogues with an Embodied Conversational Agent (ECA) in the domain of healthy-eating. The dialogues are coded in terms of Dialogue Acts associated to each system or user move. Results are quite encouraging: HMMs are a powerful formalism for describing the differences in the dialogue patterns, due to the different level of engagement of users and they can be successfully employed in real-time user’s engagement detection. Though, the HMM learning process shows a lack of robustness when using low-dimensional and skewed corpora. Therefore we plan a further validation of our approach with larger corpora in the near future.
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