TY - JOUR AU - Zhan, Runzhe AU - Liu, Xuebo AU - Wong, Derek F. AU - Chao, Lidia S. PY - 2021/05/18 Y2 - 2024/03/29 TI - Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 16 SE - AAAI Technical Track on Speech and Natural Language Processing III DO - 10.1609/aaai.v35i16.17683 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17683 SP - 14310-14318 AB - 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. ER -