Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully
Simple and Broadly Applicable

Viktor Hangya, Fabienne Braune, Alexander Fraser, Hinrich Schütze

ACL 2018


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.