A central purpose of referring expressions is to distinguish intended referents from other entities that are in the context; but how is this context determined? This paper draws a distinction between discourse context ―other entities that have been mentioned in the dialogue― and visual context ―visually available objects near the intended referent. It explores how these two different aspects of context have an impact on subsequent reference in a dialogic situation where the speakers share both discourse and visual context. In addition we take into account the impact of the reference history ―forms of reference used previously in the discourse― on forming what have been called conceptual pacts. By comparing the output of different parameter settings in our model to a data set of human-produced referring expressions, we determine that an approach to subsequent reference based on conceptual pacts provides a better explanation of our data than previously proposed algorithmic approaches which compute a new distinguishing description for the intended referent every time it is mentioned.

Krahmer et al.’s (2003) graph-based framework provides an elegant and flexible approach to the generation of referring expressions. In this paper, we present the first reported study that systematically investigates how to tune the parameters of the graph-based framework on the basis of a corpus of human-generated descriptions. We focus in particular on replicating the redundant nature of human referring expressions, whereby properties not strictly necessary for identifying a referent are nonetheless included in descriptions. We show how statistics derived from the corpus data can be integrated to boost the framework’s performance over a non-stochastic baseline.