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        <title>A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG</title>
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        <namePart type="given">Jin-Ge</namePart>
        <namePart type="family">Yao</namePart>
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        <dateIssued>2019-oct–nov</dateIssued>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
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    <abstract>Automatic natural language generation systems need to use the contextually-appropriate verbs when describing different kinds of facts or events, which has triggered research interest on verb selection for data-to-text generation. In this paper, we discuss a few limitations of the current task settings and the evaluation metrics. We also provide two simple, efficient, interpretable baseline approaches for statistical selection of trend verbs, which give a strong performance on both previously used evaluation metrics and our new evaluation.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8622</identifier>
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%0 Conference Proceedings
%T A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG
%A Chen, Guanyi
%A Yao, Jin-Ge
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F chen-yao-2019-closer
%X Automatic natural language generation systems need to use the contextually-appropriate verbs when describing different kinds of facts or events, which has triggered research interest on verb selection for data-to-text generation. In this paper, we discuss a few limitations of the current task settings and the evaluation metrics. We also provide two simple, efficient, interpretable baseline approaches for statistical selection of trend verbs, which give a strong performance on both previously used evaluation metrics and our new evaluation.
%R 10.18653/v1/W19-8622
%U https://aclanthology.org/W19-8622
%U https://doi.org/10.18653/v1/W19-8622
%P 158-163

Markdown (Informal)

[A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG](https://aclanthology.org/W19-8622) (Chen & Yao, INLG 2019)

• A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG (Chen & Yao, INLG 2019)

ACL

• Guanyi Chen and Jin-Ge Yao. 2019. A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG. In Proceedings of the 12th International Conference on Natural Language Generation, pages 158–163, Tokyo, Japan. Association for Computational Linguistics.