A key task in natural language generation (NLG) is Referring Expression Generation (REG), in which a set of properties are selected to describe a target referent. Computational cognitive models of REG typically focus on REG-in-context, where the referring expressions are designed to take into account the conversational context into which they are to be generated. However, in practice, these methods only focus on linguistic context of the text into which they are to be inserted. We argue that to develop robust models of naturalistic human referring, REG will need to move beyond linguistic context, and account for cognitive and environmental context as well. That is, we propose that a cognitivist, interactionist, and situated approach to modeling REG is needed. In this paper, we present GAIA, a Givenness Hierarchy theoretic model of REG, and demonstrate the immediate qualitative benefits of this model over the traditional REG model which it extends.

Authors:

Mark Higger: Colorado School of Mines; Tom Williams: Colorado School of Mines