The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Mostafa Abdou, Vinit Ravishankar, Maria Barrett, Yonatan Belinkov, Desmond Elliott, Anders Søgaard

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Large-scale pretrained language models are the major driving force behind recent improvements in perfromance on the Winograd Schema Challenge, a widely employed test of commonsense 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.
Original languageUndefined/Unknown
Title of host publicationProceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Place of PublicationOnline
Number of pages15
StatePublished - 1 Jul 2020

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