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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 7112–7123
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.621
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.621.pdf
- Code
- luoxiaoheics/stancecs