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Multi-task transfer learning based on pre-trained language encoders achievesstate-of-the-art performance across a range of tasks. Standard approachesimplicitly assume the tasks, for which we have training data, are equallyrepresentative of the tasks we are interested in, an assumption which is oftenhard to justify. This paper presents a more agnostic approach to multi-tasktransfer learning, which uses automated curriculum learning to minimize a newfamily of worst-case-aware losses across tasks. Not only do these losses leadto better performance on outlier tasks; they also lead to better performance inzero-shot and few-shot transfer settings.

Worst-Case-Aware Curriculum Learning for Zero and Few Shot Transfer