Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach

Abstract

When giving descriptions, speakers often signify object shape or size with hand gestures. Such so-called `iconic' gestures represent their meaning through their relevance to referents in the verbal content, rather than having a conventional form. The gesture form on its own is often ambiguous, and the aspect of the referent that it highlights is constrained by what the language makes salient. We show how the verbal content guides gesture interpretation through a computational model that frames the task as a multi-label classification task that maps multimodal utterances to semantic categories, using annotated human-human data.

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