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In order to understand multimodal interactions between humans or humans and machine, it is minimally necessary to identify the content of the agents’ communicative acts in the dialogue. This can involve either overt linguistic expressions (speech or writing), content-bearing gesture, or the integration of both. But this content must be interpreted relative to a deeper understanding of an agent’s Theory of Mind (one’s mental state, desires, and intentions) in the context of the dialogue as it dynamically unfolds. This, in turn, can require identifying and tracking nonverbal behaviors, such as gaze, body posture, facial expressions, and actions, all of which contribute to understanding how expressions are contextualized in the dialogue, and interpreted relative to the epistemic attitudes of each agent. In this paper, we adopt Generative Lexicon’s approach to event structure to provide a lexical semantics for ontic and epistemic actions as used in Bolander’s interpretation of Dynamic Epistemic Logic, called Lexical Event Modeling (LEM). This allows for the compositional construction of epistemic models of a dialogue state. We demonstrate how veridical and false belief scenarios are treated compositionally within this model.
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Common Ground Tracking in Multimodal Dialogue
Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on “dialogue state tracking” (DST), which is the ability to update the representations of the speaker’s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is “common ground tracking” (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and “questions under discussion” (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.
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- Award ID(s):
- 2326985
- PAR ID:
- 10539987
- Publisher / Repository:
- ACL
- Date Published:
- Format(s):
- Medium: X
- Location:
- Turin, Italy
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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