Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable
Viktor Hangya, Fabienne Braune, Alexander Fraser, Hinrich Schütze
Abstract
Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here. We make two contributions. First, we test a delightfully simple method for domain adaptation of bilingual word embeddings. We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction. Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks. We show that this method also helps in low-resource setups. Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.- Anthology ID:
- P18-1075
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 810–820
- Language:
- URL:
- https://aclanthology.org/P18-1075
- DOI:
- 10.18653/v1/P18-1075
- Cite (ACL):
- Viktor Hangya, Fabienne Braune, Alexander Fraser, and Hinrich Schütze. 2018. Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 810–820, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (Hangya et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1075.pdf
- Code
- hangyav/biadapt