Infusing Knowledge from Wikipedia to Enhance Stance Detection

Zihao He, Negar Mokhberian, Kristina Lerman


Abstract
Stance detection infers a text author’s attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.
Anthology ID:
2022.wassa-1.7
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
71–77
Language:
URL:
https://aclanthology.org/2022.wassa-1.7
DOI:
10.18653/v1/2022.wassa-1.7
Bibkey:
Cite (ACL):
Zihao He, Negar Mokhberian, and Kristina Lerman. 2022. Infusing Knowledge from Wikipedia to Enhance Stance Detection. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 71–77, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Infusing Knowledge from Wikipedia to Enhance Stance Detection (He et al., WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.7.pdf
Video:
 https://aclanthology.org/2022.wassa-1.7.mp4
Code
 zihaohe123/wiki-enhanced-stance-detection +  additional community code