  Modern neural networks do not always produce well-calibrated predictions,
even when trained with a proper scoring function such as cross-entropy. In
classification settings, simple methods such as isotonic regression or
temperature scaling may be used in conjunction with a held-out dataset to
calibrate model outputs. However, extending these methods to structured
prediction is not always straightforward or effective; furthermore, a held-out
calibration set may not always be available. In this paper, we study ensemble
distillation as a general framework for producing well-calibrated structured
prediction models while avoiding the prohibitive inference-time cost of
ensembles. We validate this framework on two tasks: named-entity recognition
and machine translation. We find that, across both tasks, ensemble distillation
produces models which retain much of, and occasionally improve upon, the
performance and calibration benefits of ensembles, while only requiring a
single model during test-time.

Machine Learning;Computation and Language;Machine Learning