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2021 EACL EACL 2021

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

Abstract

AbstractCross-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.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Costanza Conforti , Jakob Berndt , Marco Basaldella , Mohammad Taher Pilehvar , Chryssi Giannitsarou , Flavio Toxvaerd , Nigel Collier

Topics

Machine Learning > Learning Types > Adversarial Learning Natural Language Processing > Applications > Text Classification

Keywords

stance detection adversarial training
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