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        <title>Statistical NLG for Generating the Content and Form of Referring Expressions</title>
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        <namePart type="given">Xiao</namePart>
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        <namePart type="given">Kees</namePart>
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    <name type="personal">
        <namePart type="given">Chenghua</namePart>
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            <title>Proceedings of the 11th International Conference on Natural Language Generation</title>
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            <namePart type="given">Emiel</namePart>
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            <namePart type="given">Albert</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6561</identifier>
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%0 Conference Proceedings
%T Statistical NLG for Generating the Content and Form of Referring Expressions
%A Li, Xiao
%A van Deemter, Kees
%A Lin, Chenghua
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F li-etal-2018-statistical
%X 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.
%R 10.18653/v1/W18-6561
%U https://aclanthology.org/W18-6561
%U https://doi.org/10.18653/v1/W18-6561
%P 482-491

Markdown (Informal)

[Statistical NLG for Generating the Content and Form of Referring Expressions](https://aclanthology.org/W18-6561) (Li et al., INLG 2018)

• Statistical NLG for Generating the Content and Form of Referring Expressions (Li et al., INLG 2018)

ACL

• Xiao Li, Kees van Deemter, and Chenghua Lin. 2018. Statistical NLG for Generating the Content and Form of Referring Expressions. In Proceedings of the 11th International Conference on Natural Language Generation, pages 482–491, Tilburg University, The Netherlands. Association for Computational Linguistics.