  The Winograd Schema Challenge (WSC) and variants inspired by it have become
important benchmarks for common-sense reasoning (CSR). Model performance on the
WSC has quickly progressed from chance-level to near-human using neural
language models trained on massive corpora. In this paper, we analyze the
effects of varying degrees of overlap between these training corpora and the
test instances in WSC-style tasks. We find that a large number of test
instances overlap considerably with the corpora on which state-of-the-art
models are (pre)trained, and that a significant drop in classification accuracy
occurs when we evaluate models on instances with minimal overlap. Based on
these results, we develop the KnowRef-60K dataset, which consists of over 60k
pronoun disambiguation problems scraped from web data. KnowRef-60K is the
largest corpus to date for WSC-style common-sense reasoning and exhibits a
significantly lower proportion of overlaps with current pretraining corpora.
