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arXiv · 1904.09705

Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge

Yu-Ping Ruan·Xiaodan Zhu·Zhen-Hua Ling·Zhan Shi·Quan Liu·Si Wei
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Abstract

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.

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Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei. 2019-04-22. Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge. https://arxiv.org/abs/1904.09705

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