Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-ofdomain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domainshared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pretrain in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from indomain to out-of-domain and then vice versa) based on knowledge distillation until the indomain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the indomain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.
Similar work includes Iterative Dual Domain Adaptation REF and Domain Transformation Networks .
Iterative Dual Domain Adaptation for Neural Machine Translation
ArXiv
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Computer Science