Generating referring expressions in context: the GREC task evaluation challenges

Authors:
Anja Belz;Eric Kow;Jette Viethen;Albert Gatt
Affiliations:
NLTG, School of Computing, Mathematical and Information Sciences, University of Brighton, Brighton, UK;NLTG, School of Computing, Mathematical and Information Sciences, University of Brighton, Brighton, UK;Macquarie University, Sydney, NSW, Australia;Institute of Linguistics, Centre for Communication Technology, University of Malta and Communication and Cognition, Faculty of Arts, Tilburg University, The Netherlands
Venue:
Empirical methods in natural language generation
Year:
2010

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Abstract

Until recently, referring expression generation (reg) research focused on the task of selecting the semantic content of definite mentions of listener-familiar discourse entities. In the grec research programme we have been interested in a version of the reg problem definition that is (i) grounded within discourse context, (ii) embedded within an application context, and (iii) informed by naturally occurring data. This paper provides an overview of our aims and motivations in this research programme, the data resources we have built, and the first three sharedtask challenges, GREC-MSR'08, GREC-MSR'09 and GEEC-NEG'09, we have run based on the data.