@InProceedings{hangya-EtAl:2018:Long,
  author    = {Hangya, Viktor  and  Braune, Fabienne  and  Fraser, Alexander  and  Schütze, Hinrich},
  title     = {Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {810--820},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-1075}
}