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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 bothdomains seen in the training data and unseen domains. Second, we want oursystems to be adaptive, i.e., making it possible to finetune systems with justhundreds of in-domain parallel sentences. We study the domain adaptability ofmeta-learning when improving the domain robustness of the model. In this paper,we propose a novel approach, RMLNMT (Robust Meta-Learning Framework for NeuralMachine Translation Domain Adaptation), which improves the robustness ofexisting meta-learning models. More specifically, we show how to use a domainclassifier in curriculum learning and we integrate the word-level domain mixingmodel into the meta-learning framework with a balanced sampling strategy.Experiments on English$\rightarrow$German and English$\rightarrow$Chinesetranslation show that RMLNMT improves in terms of both domain robustness anddomain adaptability in seen and unseen domains. Our source code is available athttps://github.com/lavine-lmu/RMLNMT.

Improving Both Domain Robustness and Domain Adaptability in Machine Translation