Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable
Viktor Hangya, Fabienne Braune, Alexander D. Fraser, Hinrich Schütze · 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.