Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
This addresses domain adaptation challenges in low-resource neural machine translation, though it appears incremental as it builds on existing meta-learning approaches.
The paper tackles the problem of meta-trained neural machine translation failing to improve performance on domains unseen during meta-training by proposing meta-curriculum learning, which improves translation performance on both familiar and unfamiliar domains, as shown in experiments across 10 low-resource domains.
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.