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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.wassa-1.19.pdf