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

Castro Ferreira,Thiago
; 
Krahmer,Emiel
; 
Wubben,Sander
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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Date
2016-08
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Association for Computational Linguistics (ACL)
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Tilburg University Research Output Collection
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Citation
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. < https://www.aclweb.org/anthology/P/P16/P16-1054.pdf >
URI
https://hdl.handle.net/20.500.14602/20903
License
info:eu-repo/semantics/openAccess
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