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            <title>Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
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        <name type="personal">
            <namePart type="given">Giovanni</namePart>
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    <abstract>Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5007</identifier>
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%0 Conference Proceedings
%T Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
%A Park, ChaeHun
%A Yang, Wonsuk
%A Park, Jong
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F park-etal-2019-generating
%X Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions. A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data. In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem. Our model, ArgDiver (Argument generation model from diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments. At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models. We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.
%R 10.18653/v1/D19-5007
%U https://aclanthology.org/D19-5007
%U https://doi.org/10.18653/v1/D19-5007
%P 56-65

Markdown (Informal)

[Generating Sentential Arguments from Diverse Perspectives on Controversial Topic](https://aclanthology.org/D19-5007) (Park et al., NLP4IF 2019)

• Generating Sentential Arguments from Diverse Perspectives on Controversial Topic (Park et al., NLP4IF 2019)

ACL

• ChaeHun Park, Wonsuk Yang, and Jong Park. 2019. Generating Sentential Arguments from Diverse Perspectives on Controversial Topic. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 56–65, Hong Kong, China. Association for Computational Linguistics.