https://medium.com/ai%C2%B3-theory-practice-business/want-to-know-the-trick-to-achieve-robust-winograd-schema-challenge-results-4569a3bbd

In this paper, we show that the performance of three language models on WSC273 strongly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.2% and 71.9% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.5% and 6.8%, respectively