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        <title>Stance Prediction for Contemporary Issues: Data and Experiments</title>
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            <title>Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media</title>
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            <namePart type="given">Lun-Wei</namePart>
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    <abstract>We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.</abstract>
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%0 Conference Proceedings
%T Stance Prediction for Contemporary Issues: Data and Experiments
%A Hosseinia, Marjan
%A Dragut, Eduard
%A Mukherjee, Arjun
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hosseinia-etal-2020-stance
%X We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.
%R 10.18653/v1/2020.socialnlp-1.5
%U https://aclanthology.org/2020.socialnlp-1.5
%U https://doi.org/10.18653/v1/2020.socialnlp-1.5
%P 32-40

Markdown (Informal)

[Stance Prediction for Contemporary Issues: Data and Experiments](https://aclanthology.org/2020.socialnlp-1.5) (Hosseinia et al., SocialNLP 2020)

• Stance Prediction for Contemporary Issues: Data and Experiments (Hosseinia et al., SocialNLP 2020)

ACL

• Marjan Hosseinia, Eduard Dragut, and Arjun Mukherjee. 2020. Stance Prediction for Contemporary Issues: Data and Experiments. In Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media, pages 32–40, Online. Association for Computational Linguistics.