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[Research] Want to Know the Trick to Achieve Robust Winograd Schema Challenge Results?

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

submitted by /u/cdossman
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.