Production of Referring Expressions for an Unknown Audience: a Computational Model of Communal Common Ground

Roman Kutlak, Kees van Deemter, Chris Mellish

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Abstract

This article presents a computational model of the production of referring expressions under uncertainty over the hearer’s knowledge. Although such situations 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.
Original languageEnglish
Article number1275
Pages (from-to)1-18
Number of pages18
JournalFrontiers in Psychology
Volume7
DOIs
Publication statusPublished - 31 Aug 2016

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Keywords

  • generation of referring expressions
  • computational model
  • common ground
  • audience design
  • corpus

ASJC Scopus subject areas

  • Computer Science(all)
  • Psychology(all)

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