Improving Both Domain Robustness and Domain Adaptability in Machine Translation

We consider two problems of NMT ___domain adaptation using meta-learning. First, we want to reach ___domain robustness, i.e., we want to reach high quality on both domains seen in the training data and unseen domains. Second, we want our systems to be adaptive, i.e., making it possible to finetune systems with just hundreds of in-___domain parallel sentences. We study the ___domain adaptability of meta-learning when improving the ___domain robustness of the model. In this paper, we propose a novel approach, RMLNMT (Robust Meta-Learning Framework for Neural Machine Translation Domain Adaptation), which improves the robustness of existing meta-learning models. More specifically, we show how to use a ___domain classifier in curriculum learning and we integrate the word-level ___domain mixing model into the meta-learning framework with a balanced sampling strategy. Experiments on EnglishrightarrowGerman and EnglishrightarrowChinese translation show that RMLNMT improves in terms of both ___domain robustness and ___domain adaptability in seen and unseen domains. Our source code is available at https://github.com/lavine-lmu/RMLNMT.