Abstract
While there is much research on cross-domain text classification, most existing approaches focus on one-to-one or many-to-one domain adaptation. In this paper, we tackle the more challenging task of domain generalization, in which domain-invariant representations are learned from multiple source domains, without access to any data from the target domains, and classification decisions are then made on test documents in unseen target domains. We propose a novel framework based on supervised contrastive learning with a memory-saving queue. In this way, we explicitly encourage examples of the same class to be closer and examples of different classes to be further apart in the embedding space. We have conducted extensive experiments on two Amazon review sentiment datasets, and one rumour detection dataset. Experimental results show that our domain generalization method consistently outperforms state-of-the-art domain adaptation methods.- Anthology ID:
- 2022.coling-1.602
- 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:
- 6916–6926
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.602
- DOI:
- Cite (ACL):
- Qingyu Tan, Ruidan He, Lidong Bing, and Hwee Tou Ng. 2022. Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6916–6926, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (Tan et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.coling-1.602.pdf
- Code
- tonytan48/mscl