TY - GEN
T1 - Enhancing Zero-Shot Stance Detection via Targeted Background Knowledge
AU - Zhu, Qinglin
AU - Liang, Bin
AU - Sun, Jingyi
AU - Du, Jiachen
AU - Zhou, Lanjun
AU - Xu, Ruifeng
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/7/7
Y1 - 2022/7/7
N2 - 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.
AB - 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.
KW - background knowledge
KW - stance detection
KW - zero-shot stance detection
UR - https://www.scopus.com/pages/publications/85135080216
U2 - 10.1145/3477495.3531807
DO - 10.1145/3477495.3531807
M3 - 会议稿件
AN - SCOPUS:85135080216
T3 - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 2070
EP - 2075
BT - SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Y2 - 11 July 2022 through 15 July 2022
ER -