Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection

Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, Weiping Wang


Abstract
Stance detection aims to identify the attitude from an opinion towards a certain target. Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier. Therefore, this paper is devoted to few-shot stance detection and investigating how to achieve satisfactory results in semi-supervised settings. As a target-oriented task, the core idea of semi-supervised few-shot stance detection is to make better use of target-relevant information from labeled and unlabeled data. Therefore, we develop a novel target-aware semi-supervised framework. Specifically, we propose a target-aware contrastive learning objective to learn more distinguishable representations for different targets. Such an objective can be easily applied with or without unlabeled data. Furthermore, to thoroughly exploit the unlabeled data and facilitate the model to learn target-relevant stance features in the opinion content, we explore a simple but effective target-aware consistency regularization combined with a self-training strategy. The experimental results demonstrate that our approach can achieve state-of-the-art performance on multiple benchmark datasets in the few-shot setting.
Anthology ID:
2022.coling-1.605
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6944–6954
Language:
URL:
https://aclanthology.org/2022.coling-1.605
DOI:
Bibkey:
Cite (ACL):
Rui Liu, Zheng Lin, Huishan Ji, Jiangnan Li, Peng Fu, and Weiping Wang. 2022. Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6944–6954, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection (Liu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.605.pdf
Code
 monolith-v1/stcc