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    <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.</abstract>
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%0 Conference Proceedings
%T Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
%A Conforti, Costanza
%A Berndt, Jakob
%A Basaldella, Marco
%A Pilehvar, Mohammad Taher
%A Giannitsarou, Chryssi
%A Toxvaerd, Flavio
%A Collier, Nigel
%Y Toivonen, Hannu
%Y Boggia, Michele
%S Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F conforti-etal-2021-adversarial
%X 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.
%U https://aclanthology.org/2021.hackashop-1.1
%P 1-7

Markdown (Informal)

[Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.](https://aclanthology.org/2021.hackashop-1.1) (Conforti et al., Hackashop 2021)

• Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus. (Conforti et al., Hackashop 2021)

ACL

• Costanza Conforti, Jakob Berndt, Marco Basaldella, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2021. Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 1–7, Online. Association for Computational Linguistics.