Previous studies on the domain adaptation for neural machine translation
(NMT) mainly focus on the one-pass transferring out-of-domain translation
knowledge to in-domain NMT model. In this paper, we argue that such a strategy
fails to fully extract the domain-shared 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 pre-train in-domain
and out-of-domain NMT models using their own training corpora respectively, and
then iteratively perform bidirectional translation knowledge transfer (from
in-domain to out-of-domain and then vice versa) based on knowledge distillation
until the in-domain 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 in-domain 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.

本文提出一种双重迭代领域自适应框架，通过多层反向翻译知识传输来提高公共领域的翻译知识的提取效果，并通过将这种转移策略应用于多个不同相似度的领域进行了验证。该框架在中英文和英德文翻译任务中取得了令人满意的实验结果。