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        <title>A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems</title>
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    <name type="personal">
        <namePart type="given">Craig</namePart>
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        <namePart type="given">Ehud</namePart>
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            <title>Proceedings of the 13th International Conference on Natural Language Generation</title>
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        <name type="personal">
            <namePart type="given">Brian</namePart>
            <namePart type="family">Davis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yvette</namePart>
            <namePart type="family">Graham</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">John</namePart>
            <namePart type="family">Kelleher</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yaji</namePart>
            <namePart type="family">Sripada</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.</abstract>
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    <identifier type="doi">10.18653/v1/2020.inlg-1.22</identifier>
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        <date>2020-12</date>
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            <start>158</start>
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%0 Conference Proceedings
%T A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems
%A Thomson, Craig
%A Reiter, Ehud
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F thomson-reiter-2020-gold
%X Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.
%R 10.18653/v1/2020.inlg-1.22
%U https://aclanthology.org/2020.inlg-1.22/
%U https://doi.org/10.18653/v1/2020.inlg-1.22
%P 158-168

Markdown (Informal)

[A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems](https://aclanthology.org/2020.inlg-1.22/) (Thomson & Reiter, INLG 2020)

• A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems (Thomson & Reiter, INLG 2020)

ACL

• Craig Thomson and Ehud Reiter. 2020. A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems. In Proceedings of the 13th International Conference on Natural Language Generation, pages 158–168, Dublin, Ireland. Association for Computational Linguistics.