GRE3D7

A Corpus of distinguishing descriptions for objects in visual scenes

Jette Viethen, Robert Dale

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Abstract

Recent years have seen a trend towards empirically motivated and more data-driven approaches in the field of referring expression generation (REG). Much of this work has focussed on initial reference to objects in visual scenes. While this scenario of use is one of the strongest contenders for real-world applications of referring expression generation, existing data sets still only embody very simple stimulus scenes. To move this research forward, we require data sets built around increasingly complex scenes, and we need much larger data sets to accommodate their higher dimensionality. To control the complexity, we also need to adopt a hypothesis-driven approach to scene design. In this paper, we describe GRE3D7, the largest corpus of humanproduced distinguishing descriptions available to date, discuss the hypotheses that underlie its design, and offer a number of analyses of the 4480 descriptions it contains.
Original languageEnglish
Title of host publicationProceedings of the UCNLG+Eval
Subtitle of host publicationLanguage Generation and Evaluation Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages12-22
Number of pages11
ISBN (Print)9781937284183
Publication statusPublished - 2011
EventWorkshop on Language Generation and Evaluation - Edinburgh
Duration: 31 Jul 201131 Jul 2011

Workshop

WorkshopWorkshop on Language Generation and Evaluation
CityEdinburgh
Period31/07/1131/07/11

Bibliographical note

Copyright the Publisher 2011. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Cite this

Viethen, J., & Dale, R. (2011). GRE3D7: A Corpus of distinguishing descriptions for objects in visual scenes. In Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop (pp. 12-22). Association for Computational Linguistics (ACL).