Repository logo
 

Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Conforti, C 
Berndt, J 
Pilehvar, MT 
Basaldella, M 
Giannitsarou, Chryssi  ORCID logo  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.

Description

Keywords

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

Conference Name

Hackashop 2021 at 16th conference of the European Chapter of the Association for Computational Linguistics, EACL 2021 - Proceedings

Journal ISSN

Volume Title

Publisher

Association for Computational Linguistics