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Multiple choice questions (MCQs) are an efficient and common way to assessreading comprehension (RC). Every MCQ needs a set of distractor answers thatare incorrect, but plausible enough to test student knowledge. Distractorgeneration (DG) models have been proposed, and their performance is typicallyevaluated using machine translation (MT) metrics. However, MT metrics oftenmisjudge the suitability of generated distractors. We propose DISTO: the firstlearned evaluation metric for generated distractors. We validate DISTO byshowing its scores correlate highly with human ratings of distractor quality.At the same time, DISTO ranks the performance of state-of-the-art DG modelsvery differently from MT-based metrics, showing that MT metrics should not beused for distractor evaluation.

DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach