Talking about the world with a distributed model

Abstract

We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like ``the big mug'' or ``it'' typically contain content words (``big'', ``mug''), which are notoriously fuzzy or vague in their meaning, and also fun-ction words (``the'', ``it'') that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts.

Tasks

Reproductions