Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation


  • Runzhe Zhan University of Macau
  • Xuebo Liu University of Macau
  • Derek F. Wong University of Macau
  • Lidia S. Chao University of Macau



Machine Translation & Multilinguality


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




How to Cite

Zhan, R., Liu, X., Wong, D. F., & Chao, L. S. (2021). Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14310-14318.



AAAI Technical Track on Speech and Natural Language Processing III