Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
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Peer-reviewed
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Authors
Conforti, C
Berndt, J
Pilehvar, MT
Basaldella, M
Giannitsarou, Chryssi https://orcid.org/0000-0002-1488-2433
Abstract
Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.
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Journal Title
EACL Hackashop on News Media Content Analysis and Automated Report Generation, Hackashop 2021 at 16th conference of the European Chapter of the Association for Computational Linguistics, EACL 2021 - Proceedings
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Hackashop 2021 at 16th conference of the European Chapter of the Association for Computational Linguistics, EACL 2021 - Proceedings
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Association for Computational Linguistics