Towards more variation in text generation

Developing and evaluating variation models for choice of referential form

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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

Fingerprint

Recurrent neural networks
Experiments

Cite this

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). Berlin, Germany: Association for Computational Linguistics (ACL).
Castro Ferreira, Thiago ; Krahmer, Emiel ; Wubben, Sander. / Towards more variation in text generation : Developing and evaluating variation models for choice of referential form. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany : Association for Computational Linguistics (ACL), 2016. pp. 568-577
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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.",
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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. Association for Computational Linguistics (ACL), Berlin, Germany, pp. 568-577, Annual Meeting of the Association for Computational Linguistics 2016, Berlin, Germany, 7/08/16.

Towards more variation in text generation : Developing and evaluating variation models for choice of referential form. / Castro Ferreira, Thiago; Krahmer, Emiel; Wubben, Sander.

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany : Association for Computational Linguistics (ACL), 2016. p. 568-577.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Castro Ferreira T, Krahmer E, Wubben S. 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. Berlin, Germany: Association for Computational Linguistics (ACL). 2016. p. 568-577