用强化学习优化多语言翻译训练顺序，提升低资源语言翻译效果。
Optimizing the Training Schedule of Multilingual NMT using Reinforcement Learning
- 用强化学习动态调整不同语言数据的训练顺序。
- 在8对1的多语言翻译任务中，BLEU和COMET得分显著提升。
- 适合关注低资源语言翻译效率的研究者与工程师。
多语言神经机器翻译（Multilingual NMT）在拥有同一语系高资源语言（HRLs）数据时，可有效支持低资源语言（LRLs）翻译。然而，训练过程中语言呈现顺序对系统性能有显著影响。本文在一对多翻译设置下，提出两种基于强化学习的训练调度优化方法：(1) 教师-学生课程学习，利用指数平滑估计每种语言在单语或跨语言开发集上的损失回报；(2) 深度Q网络，通过额外神经网络从历史动作与奖励中学习策略。在包含8种低资源语言与1种高资源语言的8-to-1数据集上，第二种方法相较随机选择单语批次和打乱的多语批次，在调整低资源与高资源语言批次呈现次数后，实现了更高的BLEU和COMET评分。
原文摘要 · Abstract (English)
Multilingual NMT is a viable solution for translating low-resource languages (LRLs) when data from high-resource languages (HRLs) from the same language family is available. However, the training schedule, i.e. the order of presentation of languages, has an impact on the quality of such systems. Here, in a many-to-one translation setting, we propose to apply two algorithms that use reinforcement learning to optimize the training schedule of NMT: (1) Teacher-Student Curriculum Learning and (2) Deep Q Network. The former uses an exponentially smoothed estimate of the returns of each action based on the loss on monolingual or multilingual development subsets, while the latter estimates rewards using an additional neural network trained from the history of actions selected in different states of the system, together with the rewards received. On a 8-to-1 translation dataset with LRLs and HRLs, our second method improves BLEU and COMET scores with respect to both random selection of monolingual batches and shuffled multilingual batches, by adjusting the number of presentations of LRL vs. HRL batches.
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