Statistical NLG for Generating the Content and Form of Referring Expressions

Xiao Li, Kees Jacobus Van Deemter, Chenghua Lin

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Abstract

This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations suggest that the attribute selection aspect of the algorithm exceeds classic REG algorithms, while the Noun Phrases generated are as similar to those in a previously developed corpus as were Noun Phrases produced by a new set of human speakers.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Natural Language Generation
EditorsEmiel Krahmer, Albert Gatt, Martijn Goudbeek
PublisherAssociation for Computational Linguistics (ACL)
Pages482-291
Number of pages10
ISBN (Print)9781948087865
Publication statusPublished - Nov 2018
Event11th International Conference on Natural Language Generation (INLG 2018) - Tilburg University, Tilburg, Netherlands
Duration: 5 Nov 20188 Nov 2018

Conference

Conference11th International Conference on Natural Language Generation (INLG 2018)
CountryNetherlands
CityTilburg
Period5/11/188/11/18

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Li, X., Van Deemter, K. J., & Lin, C. (2018). Statistical NLG for Generating the Content and Form of Referring Expressions. In E. Krahmer, A. Gatt, & M. Goudbeek (Eds.), Proceedings of the 11th International Conference on Natural Language Generation (pp. 482-291). Association for Computational Linguistics (ACL).