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Performance on the Winograd Schema Challenge (WSC), a respected Englishcommonsense reasoning benchmark, recently rocketed from chance accuracy to 89%on the SuperGLUE leaderboard, with relatively little corroborating evidence ofa correspondingly large improvement in reasoning ability. We hypothesize thatmuch of this improvement comes from recent changes in task formalization---thecombination of input specification, loss function, and reuse of pretrainedparameters---by users of the dataset, rather than improvements in thepretrained model's reasoning ability. We perform an ablation on two WinogradSchema datasets that interpolates between the formalizations used before andafter this surge, and find (i) framing the task as multiple choice improvesperformance by 2-6 points and (ii) several additional techniques, including thereuse of a pretrained language modeling head, can mitigate the model's extremesensitivity to hyperparameters. We urge future benchmark creators to imposeadditional structure to minimize the impact of formalization decisions onreported results.

Precise Task Formalization Matters in Winograd Schema Evaluations