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A Surprisingly Robust Trick for Winograd Schema Challenge

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arxiv 1905.06290 v2 pith:ODRG76H3 submitted 2019-05-15 cs.CL

A Surprisingly Robust Trick for Winograd Schema Challenge

Vid Kocijan , Ana-Maria Cretu , Oana-Maria Camburu , Yordan Yordanov , Thomas Lukasiewicz This is my paper
classification cs.CL
keywords datasetwsc273languagechallengefine-tunedintroducedmodelsrobust
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. 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 72.5% and 74.7% on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8% and 9.6%, respectively. Furthermore, our fine-tuned models are also consistently more robust on the "complex" subsets of WSC273, introduced by Trichelair et al. (2018).

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