Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.
Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, Nigel Collier
Abstract
Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.- Anthology ID:
- 2021.wassa-1.19
- Volume:
- Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Orphee De Clercq, Alexandra Balahur, Joao Sedoc, Valentin Barriere, Shabnam Tafreshi, Sven Buechel, Veronique Hoste
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 181–187
- Language:
- URL:
- https://aclanthology.org/2021.wassa-1.19
- DOI:
- Cite (ACL):
- Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2021. Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 181–187, Online. Association for Computational Linguistics.
- Cite (Informal):
- Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus. (Conforti et al., WASSA 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.wassa-1.19.pdf