  Multiple choice questions (MCQs) are an efficient and common way to assess
reading comprehension (RC). Every MCQ needs a set of distractor answers that
are incorrect, but plausible enough to test student knowledge. Distractor
generation (DG) models have been proposed, and their performance is typically
evaluated using machine translation (MT) metrics. However, MT metrics often
misjudge the suitability of generated distractors. We propose DISTO: the first
learned evaluation metric for generated distractors. We validate DISTO by
showing its scores correlate highly with human ratings of distractor quality.
At the same time, DISTO ranks the performance of state-of-the-art DG models
very differently from MT-based metrics, showing that MT metrics should not be
used for distractor evaluation.

Computation and Language;Artificial Intelligence