To train neural machine translation models simultaneously on multiple tasks
(languages), it is common to sample each task uniformly or in proportion to
dataset sizes. As these methods offer little control over performance
trade-offs, we explore different task scheduling approaches. We first consider
existing non-adaptive techniques, then move on to adaptive schedules that
over-sample tasks with poorer results compared to their respective baseline. As
explicit schedules can be inefficient, especially if one task is highly
over-sampled, we also consider implicit schedules, learning to scale learning
rates or gradients of individual tasks instead. These techniques allow training
multilingual models that perform better for low-resource language pairs (tasks
with small amount of data), while minimizing negative effects on high-resource
tasks.

研究了不同的任务调度方法以在多个任务（语言）上同时训练神经机器翻译模型，包括现有的非自适应技术和自适应调度技术，并考虑了隐式调度技术。这些方法使得多语言模型在低资源语言对（少量数据的任务）中表现更好，同时最小化对高资源任务的负面影响。