  Large-scale pretrained language models are the major driving force behind
recent improvements in performance on the Winograd Schema Challenge, a widely
employed test of common sense reasoning ability. We show, however, with a new
diagnostic dataset, that these models are sensitive to linguistic perturbations
of the Winograd examples that minimally affect human understanding. Our results
highlight interesting differences between humans and language models: language
models are more sensitive to number or gender alternations and synonym
replacements than humans, and humans are more stable and consistent in their
predictions, maintain a much higher absolute performance, and perform better on
non-associative instances than associative ones. Overall, humans are correct
more often than out-of-the-box models, and the models are sometimes right for
the wrong reasons. Finally, we show that fine-tuning on a large, task-specific
dataset can offer a solution to these issues.
