文章基本信息

标题：Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.
本地全文：下载
作者：Costanza Conforti ; Jakob Berndt ; Marco Basaldella 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：1-7
语种：English
出版社：ACL Anthology
摘要：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.