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

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection