Towards more variation in text generation: Developing and evaluating variation models for choice of referential form

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12 Citations (Scopus)

Abstract

In this study, we introduce a nondeterministic method for referring expression generation. We describe two models that account for individual variation in the choice of referential form in automatically generated text: a Naive Bayes model and a Recurrent Neural Network. Both are evaluated using the VaREG corpus. Then we select the best performing model to generate referential forms in texts from the GREC-2.0 corpus and conduct an evaluation experiment in which humans judge the coherence and comprehensibility of the generated texts, comparing them both with the original references and those produced by a random baseline model.
Original languageEnglish
Title of host publicationProceedings of the 54th Annual Meeting of the Association for Computational Linguistics
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics (ACL)
Pages568-577
Number of pages10
Publication statusPublished - Aug 2016
EventAnnual Meeting of the Association for Computational Linguistics 2016 - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016
Conference number: 54
http://acl2016.org/

Conference

ConferenceAnnual Meeting of the Association for Computational Linguistics 2016
Abbreviated titleACL 2016
CountryGermany
CityBerlin
Period7/08/1612/08/16
Internet address

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    Castro Ferreira, T., Krahmer, E., & Wubben, S. (2016). Towards more variation in text generation: Developing and evaluating variation models for choice of referential form. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 568-577). Association for Computational Linguistics (ACL). https://www.aclweb.org/anthology/P/P16/P16-1054.pdf