  Meta-learning has been sufficiently validated to be beneficial for
low-resource neural machine translation (NMT). However, we find that
meta-trained NMT fails to improve the translation performance of the domain
unseen at the meta-training stage. In this paper, we aim to alleviate this
issue by proposing a novel meta-curriculum learning for domain adaptation in
NMT. During meta-training, the NMT first learns the similar curricula from each
domain to avoid falling into a bad local optimum early, and finally learns the
curricula of individualities to improve the model robustness for learning
domain-specific knowledge. Experimental results on 10 different low-resource
domains show that meta-curriculum learning can improve the translation
performance of both familiar and unfamiliar domains. All the codes and data are
freely available at https://github.com/NLP2CT/Meta-Curriculum.
