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        <title>Predicting the Outcome of Deliberative Democracy: A Research Proposal</title>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>As liberal states across the world face a decline in political participation by citizens, deliberative democracy is a promising solution for the public’s decreasing confidence and apathy towards the democratic process. Deliberative dialogue is method of public interaction that is fundamental to the concept of deliberative democracy. The ability to identify and predict consensus in the dialogues could bring greater accessibility and transparency to the face-to-face participatory process. The paper sets out a research plan for the first steps at automatically identifying and predicting consensus in a corpus of German language debates on hydraulic fracking. It proposes the use of a unique combination of lexical, sentiment, durational and further ‘derivative’ features of adjacency pairs to train traditional classification models. In addition to this, the use of deep learning techniques to improve the accuracy of the classification and prediction tasks is also discussed. Preliminary results at the classification of utterances are also presented, with an F1 between 0.61 and 0.64 demonstrating that the task of recognising agreement is demanding but possible.</abstract>
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    <identifier type="doi">10.18653/v1/P19-2013</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Predicting the Outcome of Deliberative Democracy: A Research Proposal
%A McKillop, Conor
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F mckillop-2019-predicting
%X As liberal states across the world face a decline in political participation by citizens, deliberative democracy is a promising solution for the public’s decreasing confidence and apathy towards the democratic process. Deliberative dialogue is method of public interaction that is fundamental to the concept of deliberative democracy. The ability to identify and predict consensus in the dialogues could bring greater accessibility and transparency to the face-to-face participatory process. The paper sets out a research plan for the first steps at automatically identifying and predicting consensus in a corpus of German language debates on hydraulic fracking. It proposes the use of a unique combination of lexical, sentiment, durational and further ‘derivative’ features of adjacency pairs to train traditional classification models. In addition to this, the use of deep learning techniques to improve the accuracy of the classification and prediction tasks is also discussed. Preliminary results at the classification of utterances are also presented, with an F1 between 0.61 and 0.64 demonstrating that the task of recognising agreement is demanding but possible.
%R 10.18653/v1/P19-2013
%U https://aclanthology.org/P19-2013
%U https://doi.org/10.18653/v1/P19-2013
%P 100-105

Markdown (Informal)

[Predicting the Outcome of Deliberative Democracy: A Research Proposal](https://aclanthology.org/P19-2013) (McKillop, ACL 2019)

• Predicting the Outcome of Deliberative Democracy: A Research Proposal (McKillop, ACL 2019)

ACL

• Conor McKillop. 2019. Predicting the Outcome of Deliberative Democracy: A Research Proposal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 100–105, Florence, Italy. Association for Computational Linguistics.