Currently, multilingual machine translation is receiving more and more
attention since it brings better performance for low resource languages (LRLs)
and saves more space. However, existing multilingual machine translation models
face a severe challenge: imbalance. As a result, the translation performance of
different languages in multilingual translation models are quite different. We
argue that this imbalance problem stems from the different learning
competencies of different languages. Therefore, we focus on balancing the
learning competencies of different languages and propose Competence-based
Curriculum Learning for Multilingual Machine Translation, named CCL-M.
Specifically, we firstly define two competencies to help schedule the high
resource languages (HRLs) and the low resource languages: 1) Self-evaluated
Competence, evaluating how well the language itself has been learned; and 2)
HRLs-evaluated Competence, evaluating whether an LRL is ready to be learned
according to HRLs' Self-evaluated Competence. Based on the above competencies,
we utilize the proposed CCL-M algorithm to gradually add new languages into the
training set in a curriculum learning manner. Furthermore, we propose a novel
competenceaware dynamic balancing sampling strategy for better selecting
training samples in multilingual training. Experimental results show that our
approach has achieved a steady and significant performance gain compared to the
previous state-of-the-art approach on the TED talks dataset.

本文提出了一种基于能力的课程学习算法 CCL-M，通过均衡不同语言的学习能力，从而达到在多语言机器翻译中提高低资源语言翻译效果的目的，并且运用该算法在动态平衡采样的基础上，对深度学习的训练集积极剔除并添加。最终在 TED talks 数据集上的实验结果表明，与之前最先进的方法相比，本文方法能够取得平稳显著的性能提升。