The Winograd Schema Challenge - a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge - was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.
翻译:2011年,Hector Levesque提出了 " Winograd Schema Challenge " (Winograd Schema Challenge) -- -- 一套配对词的双重句子,涉及使用常识知识,这似乎需要使用常识知识。 到2019年,一些基于大型培训前变压器语言模型并针对这些问题进行微调的AI系统实现了90%的准确性。在本文中,我们回顾了Winograd Schema Challenge的历史,并讨论了过去10年在WSC上进行的研究的持久贡献。我们讨论了为WSC开发的各种数据集的重要性,以及研究界对代理任务在评估AI系统情报方面的作用的深入理解。