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        <title>Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes</title>
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    <name type="personal">
        <namePart type="given">Lu</namePart>
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    <name type="personal">
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    <abstract>Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model‘s combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.</abstract>
    <identifier type="citekey">wang-etal-2017-winning</identifier>
    <identifier type="doi">10.1162/tacl_a_00057</identifier>
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        <url>https://aclanthology.org/Q17-1016/</url>
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        <date>2017</date>
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            <start>219</start>
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%0 Journal Article
%T Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes
%A Wang, Lu
%A Beauchamp, Nick
%A Shugars, Sarah
%A Qin, Kechen
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F wang-etal-2017-winning
%X Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model‘s combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.
%R 10.1162/tacl_a_00057
%U https://aclanthology.org/Q17-1016/
%U https://doi.org/10.1162/tacl_a_00057
%P 219-232

Markdown (Informal)

[Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes](https://aclanthology.org/Q17-1016/) (Wang et al., TACL 2017)

• Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes (Wang et al., TACL 2017)

ACL

• Lu Wang, Nick Beauchamp, Sarah Shugars, and Kechen Qin. 2017. Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes. Transactions of the Association for Computational Linguistics, 5:219–232.