Abstract
Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.- Anthology ID:
- R19-1025
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 213–222
- Language:
- URL:
- https://aclanthology.org/R19-1025
- DOI:
- 10.26615/978-954-452-056-4_025
- Cite (ACL):
- Xia Cui and Danushka Bollegala. 2019. Self-Adaptation for Unsupervised Domain Adaptation. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 213–222, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Self-Adaptation for Unsupervised Domain Adaptation (Cui & Bollegala, RANLP 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/R19-1025.pdf