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Contribution to Book
A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution
Proceedings of ICWS 2015 (2015)
  • Casey Kennington, Bielefeld University
  • Livia Dia, University of York
  • David Schlangen, Bielefeld University
A large part of human communication involves referring to entities in the world, and often these entities are objects that are visually present for the interlocutors. A computer system that aims to resolve such references needs to tackle a complex task: objects and their visual features must be determined, the referring expressions must be recognised, extra-linguistic information such as eye gaze or pointing gestures must be incorporated — and the intended connection between words and world must be reconstructed. In this paper, we introduce a discriminative model of reference resolution that processes incrementally (i.e., word for word), is perceptually-grounded, and improves when interpolated with information from gaze and pointing gestures. We evaluated our model and found that it performed robustly in a realistic reference resolution task, when compared to a generative model. 
Publication Date
Matthew Purver, Mehrnoosh Sadrzadeh, and Matthew Stone
Association for Computational Linguistics
Citation Information
Casey Kennington, Livia Dia and David Schlangen. "A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution" London, UKProceedings of ICWS 2015 (2015) p. 195 - 205
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