Recent studies have proven that the training of neural machine translation
(NMT) can be facilitated by mimicking the learning process of humans.
Nevertheless, achievements of such kind of curriculum learning rely on the
quality of artificial schedule drawn up with the handcrafted features, e.g.
sentence length or word rarity. We ameliorate this procedure with a more
flexible manner by proposing self-paced learning, where NMT model is allowed to
1) automatically quantify the learning confidence over training examples; and
2) flexibly govern its learning via regulating the loss in each iteration step.
Experimental results over multiple translation tasks demonstrate that the
proposed model yields better performance than strong baselines and those models
trained with human-designed curricula on both translation quality and
convergence speed.

本研究提出自适应学习方法来改进神经机器翻译 (NMT) 训练过程，允许 NMT 模型自动评估对训练示例的学习自信度并通过调节每次迭代的损失来灵活地控制学习，实验证明该方法在提高翻译质量和加速收敛速度方面优于强基线和人工设计课程训练的模型。