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        <title>Unsupervised stance detection for arguments from consequences</title>
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    <name type="personal">
        <namePart type="given">Jonathan</namePart>
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    <name type="personal">
        <namePart type="given">Heiner</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.4</identifier>
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%0 Conference Proceedings
%T Unsupervised stance detection for arguments from consequences
%A Kobbe, Jonathan
%A Hulpuș, Ioana
%A Stuckenschmidt, Heiner
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kobbe-etal-2020-unsupervised
%X Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.
%R 10.18653/v1/2020.emnlp-main.4
%U https://aclanthology.org/2020.emnlp-main.4/
%U https://doi.org/10.18653/v1/2020.emnlp-main.4
%P 50-60

Markdown (Informal)

[Unsupervised stance detection for arguments from consequences](https://aclanthology.org/2020.emnlp-main.4/) (Kobbe et al., EMNLP 2020)

• Unsupervised stance detection for arguments from consequences (Kobbe et al., EMNLP 2020)

ACL

• Jonathan Kobbe, Ioana Hulpuș, and Heiner Stuckenschmidt. 2020. Unsupervised stance detection for arguments from consequences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 50–60, Online. Association for Computational Linguistics.