Generating Expressions that Refer to Visible Objects

Margaret Mitchell, Kees Van Deemter, Ehud Reiter

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

49 Citations (Scopus)
10 Downloads (Pure)

Abstract

We introduce a novel algorithm for generating referring expressions, informed by human and computer vision and designed to refer to visible objects. Our method separates absolute properties like color from relative properties like size to stochastically generate a diverse set of outputs. Expressions generated using this method are often overspecified and may be underspecified, akin to expressions produced by people. We call such expressions identifying descriptions. The algorithm outperforms the well-known Incremental Algorithm (Dale and Reiter, 1995) and the Graph- Based Algorithm (Krahmer et al., 2003; Viethen et al., 2008) across a variety of images in two domains. We additionally motivate an evaluation method for referring expression generation that takes the proposed algorithm's non-determinism into account.

Original languageEnglish
Title of host publicationProceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
EditorsLucy Vanderwende, Hal Daumé III, Katrin Kirchhoff
PublisherAssociation for Computational Linguistics (ACL)
Pages1174-1184
Number of pages11
ISBN (Print)9781937284473
Publication statusPublished - Jun 2013
Event2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2013 - Atlanta, United States
Duration: 9 Jun 201314 Jun 2013

Conference

Conference2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2013
Country/TerritoryUnited States
CityAtlanta
Period9/06/1314/06/13

Bibliographical note

Funding for this research has been provided by SICSA and ORSAS. We thank the anonymous reviewers for useful comments on this paper.

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