Unsupervised stance detection for arguments from consequences

Kobbe, Jonathan ; Hulpus, Ioana ; Stuckenschmidt, Heiner

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URL: https://madoc.bib.uni-mannheim.de/57482
Additional URL: https://www.aclweb.org/anthology/2020.emnlp-main.4
URN: urn:nbn:de:bsz:180-madoc-574820
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: EMNLP 2020 : proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16th - 20th November 2020
Page range: 50-60
Conference title: EMNLP 2020
Location of the conference venue: Online
Date of the conference: 16.-20.11.2020
Publisher: Webber, Bonnie
Place of publication: Online
Publishing house: Association for Computational Linguistics
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
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.

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