Speaker-dependent variation in content selection for referring expression generation

Jette Viethen, Robert Dale

Research output: Contribution to journalConference paperpeer-review


In this paper we describe machine learning experiments that aim to characterise the content selection process for distinguishing descriptions. Our experiments are based on two large corpora of human-produced descriptions of objects in relatively small visual scenes; the referring expressions are annotated with their semantic content. The visual context of reference is widely considered to be a primary determinant of content in referring expression generation, so we explore whether a model can be trained to predict the collection of descriptive attributes that should be used in a given situation. Our experiments demonstrate that speaker-specific preferences play a much more important role than existing approaches to referring expression generation acknowledge.
Original languageEnglish
Pages (from-to)81-89
Number of pages9
JournalAustralasian Language Technology Workshop 2010 : proceedings of the workshop
Publication statusPublished - 2010
EventAustralasian Language Technology Workshop (8th : 2010) - Melbourne, Australia
Duration: 9 Dec 201010 Dec 2010


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