Paper Info
Title
Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge
Abstract
Stance detection aims to identify the stance of the text towards a target. Different from conventional stance detection, Zero-Shot Stance Detection (ZSSD) needs to predict the stances of the unseen targets during the inference stage. For human beings, we generally tend to reason the stance of a new target by linking it with the related knowledge learned from the known ones. Therefore, in this paper, to better generalize the target-related stance features learned from the known targets to the unseen ones, we incorporate the targeted background knowledge from Wikipedia into the model. The background knowledge can be considered as a bridge for connecting the meanings between known targets and the unseen ones, which enables the generalization and reasoning ability of the model to be improved in dealing with ZSSD. Extensive experimental results demonstrate that our model outperforms the state-of-the-art methods on the ZSSD task.
Year
DOI
Venue
2022
10.1145/3477495.3531807
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
DocType
Citations 
zero-shot stance detection, stance detection, background knowledge
Conference
0
PageRank 
References 
Authors
0.34
14
6
Name
Order
Citations
PageRank
Qinglin Zhu100.34
Liang Bin223954.58
Jingyi Sun300.34
Du Jiachen4369.02
Lanjun Zhou500.34
Xu Ruifeng643253.04