  This paper considers the unsupervised domain adaptation problem for neural
machine translation (NMT), where we assume the access to only monolingual text
in either the source or target language in the new domain. We propose a
cross-lingual data selection method to extract in-domain sentences in the
missing language side from a large generic monolingual corpus. Our proposed
method trains an adaptive layer on top of multilingual BERT by contrastive
learning to align the representation between the source and target language.
This then enables the transferability of the domain classifier between the
languages in a zero-shot manner. Once the in-domain data is detected by the
classifier, the NMT model is then adapted to the new domain by jointly learning
translation and domain discrimination tasks. We evaluate our cross-lingual data
selection method on NMT across five diverse domains in three language pairs, as
well as a real-world scenario of translation for COVID-19. The results show
that our proposed method outperforms other selection baselines up to +1.5 BLEU
score.
