Statistical NLG for Generating the Content and Form of Referring Expressions

Xiao Li; Kees Jacobus Van Deemter; Chenghua Lin

Statistical NLG for Generating the Content and Form of Referring Expressions

Xiao Li, Kees Jacobus Van Deemter, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

2 Citations (Scopus)

6 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 11th International Conference on Natural Language Generation
Editors	Emiel Krahmer, Albert Gatt, Martijn Goudbeek
Publisher	Association for Computational Linguistics (ACL)
Pages	482-291
Number of pages	10
ISBN (Print)	9781948087865
Publication status	Published - Nov 2018
Event	11th International Conference on Natural Language Generation (INLG 2018) - Tilburg University, Tilburg, Netherlands Duration: 5 Nov 2018 → 8 Nov 2018

Conference

Conference	11th International Conference on Natural Language Generation (INLG 2018)
Country/Territory	Netherlands
City	Tilburg
Period	5/11/18 → 8/11/18

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Statistical NLG for Generating the Content and Form of Referring Expressions
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https://www.aclweb.org/anthology/W18-6561Licence: CC BY

Cite this

Statistical NLG for Generating the Content and Form of Referring Expressions. / Li, Xiao; Van Deemter, Kees Jacobus; Lin, Chenghua.

Proceedings of the 11th International Conference on Natural Language Generation. ed. / Emiel Krahmer; Albert Gatt; Martijn Goudbeek. Association for Computational Linguistics (ACL), 2018. p. 482-291.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Li, X, Van Deemter, KJ & 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. Association for Computational Linguistics (ACL), pp. 482-291, 11th International Conference on Natural Language Generation (INLG 2018) , Tilburg, Netherlands, 5/11/18. <https://www.aclweb.org/anthology/W18-6561>

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title = "Statistical NLG for Generating the Content and Form of Referring Expressions",

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.",

author = "Xiao Li and {Van Deemter}, {Kees Jacobus} and Chenghua Lin",

note = "Acknowledgments We gratefully acknowledge the anonymous reviewers for their very helpful comments.; 11th International Conference on Natural Language Generation (INLG 2018) ; Conference date: 05-11-2018 Through 08-11-2018",

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N2 - 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.

AB - 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.

M3 - Published conference contribution

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BT - Proceedings of the 11th International Conference on Natural Language Generation

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A2 - Gatt, Albert

A2 - Goudbeek, Martijn

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T2 - 11th International Conference on Natural Language Generation (INLG 2018)

Y2 - 5 November 2018 through 8 November 2018

ER -

Statistical NLG for Generating the Content and Form of Referring Expressions

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