Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

Yun Luo, Zihan Liu, Yuefeng Shi, Stan Z. Li, Yue Zhang


Abstract
The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset–VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.
Anthology ID:
2022.coling-1.621
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:
7112–7123
Language:
URL:
https://aclanthology.org/2022.coling-1.621
DOI:
Bibkey:
Cite (ACL):
Yun Luo, Zihan Liu, Yuefeng Shi, Stan Z. Li, and Yue Zhang. 2022. Exploiting Sentiment and Common Sense for Zero-shot Stance Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 7112–7123, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (Luo et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.621.pdf
Code
 luoxiaoheics/stancecs