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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-1075.pdf
Presentation:
 P18-1075.Presentation.pdf
Video:
 https://vimeo.com/285800996
Code
 hangyav/biadapt