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Generation of effective referring expressions in situated context

  • In task-oriented communication, references often need to be effective in their distinctive function, that is, help the hearer identify the referent correctly and as effortlessly as possible. However, it can be challenging for computational or empirical studies to capture referential effectiveness. Empirical findings indicate that human-produced references are not always optimally effective, and that their effectiveness may depend on different aspects of the situational context that can evolve dynamically over the course of an interaction. On this basis, we propose a computational model of effective reference generation which distinguishes speaker behaviour according to its helpfulness to the hearer in a certain situation, and explicitly aims at modelling highly helpful speaker behaviour rather than speaker behaviour invariably. Our model, which extends the planning-based paradigm of sentence generation with a statistical account of effectiveness, can adapt to the situational context by making this distinction newly for each newIn task-oriented communication, references often need to be effective in their distinctive function, that is, help the hearer identify the referent correctly and as effortlessly as possible. However, it can be challenging for computational or empirical studies to capture referential effectiveness. Empirical findings indicate that human-produced references are not always optimally effective, and that their effectiveness may depend on different aspects of the situational context that can evolve dynamically over the course of an interaction. On this basis, we propose a computational model of effective reference generation which distinguishes speaker behaviour according to its helpfulness to the hearer in a certain situation, and explicitly aims at modelling highly helpful speaker behaviour rather than speaker behaviour invariably. Our model, which extends the planning-based paradigm of sentence generation with a statistical account of effectiveness, can adapt to the situational context by making this distinction newly for each new reference. We find that the generated references resemble those of effective human speakers more closely than references of baseline models, and that they are resolved correctly more often than those of other models participating in a shared-task evaluation with human hearers. Finally, we argue that the model could serve as a methodological framework for computational and empirical research on referential effectiveness.show moreshow less

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Metadaten
Author:Konstantina Garoufi, Alexander Koller
DOI:https://doi.org/10.1080/01690965.2013.847190
ISSN:2327-3798 (print)
ISSN:2327-3801 (online)
Parent Title (English):Language, cognition and neuroscience
Publisher:Routledge, Taylor & Francis Group
Place of publication:Abingdon
Document Type:Article
Language:English
Year of first Publication:2014
Year of Completion:2014
Release Date:2017/03/27
Tag:natural language generation; reference; referential effectiveness
Volume:29
Issue:8
Pagenumber:16
First Page:986
Last Page:1001
Funder:Collaborative Research Center "Information Structure: The Linguistic Means of Structuring Utterances, Sentences and Texts" at the University of Potsdam [SFB 632]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
Peer Review:Referiert
Institution name at the time of publication:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik