Unsupervised Domain Adaptation for Neural Machine Translation

Impressive neural machine translation (NMT) results are achieved in domains with large-scale, high quality bilingual training corpora. However, transferring to a target domain with significant domain shifts but no bilingual training corpora remains largely unexplored. To address the aforementioned setting of unsupervised domain adaptation, we propose a novel adversarial training procedure for NMT to leverage the widespread monolingual data in target domain. Two discriminative networks, namely the domain discriminator and pair discriminator, are introduced to guide the translation model. The domain discriminator evaluates whether the sentences generated by the translation model are indistinguishable from the ones in target domain. The pair discriminator assesses whether the generated sentences are paired with the source-side sentences. The translation model acts as an adversary to the two discriminators, which aims to generate sentences uneasily discriminated by the discriminators. We tested our approach on Chinese-English and English-German translation tasks. Experimental results show that our approaches achieve great success in unsupervised domain adaptation for NMT.