# Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge Source: [Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:32:37.817Z Authors: Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei Published: 2019-04-22T03:00:40.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Pronoun Disambiguation on Winograd Schema Challenge](): Pronoun Disambiguation; Winograd Schema Challenge; Accuracy: 71.1. - [Pronoun Disambiguation on WSC (test)](): Pronoun Disambiguation; WSC (test); Accuracy (Single): not recorded. ## Original sources and implementation [Original paper]() [PDF]()