A Formal Analysis of Multimodal Referring Strategies Under Common Ground
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Abstract (arXiv)
In this paper, we present an analysis of computationally generated mixed-modality definite referring expressions using combinations of gesture and linguistic descriptions. In doing so, we expose some striking formal semantic properties of the interactions between gesture and language, conditioned on the introduction of content into the common ground between the (computational) speaker and (human) viewer, and demonstrate how these formal features can contribute to training better models to predict viewer judgment of referring expressions, and potentially to the generation of more natural and informative referring expressions.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Mar 2020.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 1.
5 papers.
- 2024Common Ground Tracking in Multimodal Dialoguecited by 13
- 2025TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialoguescited by 9
- 2024NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interactioncited by 4
- 2025Dynamic Epistemic Friction in Dialoguecited by 4
- 2026Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetrycited by 1
- 2026Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetry5 Mar 2026
- 2025Dynamic Epistemic Friction in Dialogue12 Jun 2025
- 2025TRACE: Real-Time Multimodal Common Ground Tracking in Situated Collaborative Dialogues12 Mar 2025
- 2024Common Ground Tracking in Multimodal Dialogue26 Mar 2024
- 2024NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interaction4 Mar 2024