Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%. We demonstrate that the leading performance benefits from jointly modelling sentence structures, utilizing knowledge learned from cutting-edge pretraining models, and performing fine-tuning. We conduct detailed analyses, showing that fine-tuning is critical for achieving the performance, but it helps more on the simpler associative problems. Modelling sentence dependency structures, however, consistently helps on the harder non-associative subset of WSC. Analysis also shows that larger fine-tuning datasets yield better performances, suggesting the potential benefit of future work on annotating more Winograd schema sentences.

本文介绍了Winograd Schema Challenge中采用最新技术取得的最高准确率，并探讨了共同建模句子结构、利用先进的预训练模型学习知识以及进行微调等方法对性能的影响，并分析表明微调在较简单的联想问题中更有利，而建模句子结构则对较困难的非联想子集更有帮助。同时，本文还表明，更大的微调数据集可以带来更好的性能，这也表明了未来注释更多Winograd模式句子方面的潜在价值。

探索无监督预训练和句子结构建模用于Winograd Schema Challenge