title: Production of Referring Expressions for an Unknown Audience: A Computational Model of Communal Common Ground.
abstract: This article presents a computational model of the production of referring expressions under uncertainty over the hearer's knowledge. Although situations where the hearer's knowledge is uncertain have seldom been addressed in the computational literature, they are common in ordinary communication, for example when a writer addresses an unknown audience, or when a speaker addresses a stranger. We propose a computational model composed of three complimentary heuristics based on, respectively, an estimation of the recipient's knowledge, an estimation of the extent to which a property is unexpected, and the question of what is the optimum number of properties in a given situation. The model was tested in an experiment with human readers, in which it was compared against the Incremental Algorithm and human-produced descriptions. The results suggest that the new model outperforms the Incremental Algorithm in terms of the proportion of correctly identified entities and in terms of the perceived quality of the generated descriptions.
authors.affiliation: Natural Language Generation Group, Computing Science Department, University of Aberdeen Aberdeen, UK.
authors.forename: Roman
authors.initials: R
authors.lastname: Kutlak
authors.affiliation: Natural Language Generation Group, Computing Science Department, University of Aberdeen Aberdeen, UK.
authors.forename: Kees
authors.initials: K
authors.lastname: van Deemter
authors.affiliation: Natural Language Generation Group, Computing Science Department, University of Aberdeen Aberdeen, UK.
authors.forename: Chris
authors.initials: C
authors.lastname: Mellish
keywords: audience design; common ground; computational model; corpus; generation of referring expressions