AI Bibliography
AI Bibliography |
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Kocijan, V., Davis, E., Lukasiewicz, T., Marcus, G., & Morgenstern, L. (2023). The defeat of the winograd schema challenge. arXiv preprint arXiv:2201.02387. |
| Resource type: Journal Article BibTeX citation key: Kocijan2023 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General Subcategories: Big data, Decision making, Deep learning, Human learning, Machine intelligence, Machine learning, Neural nets Creators: Davis, Kocijan, Lukasiewicz, Marcus, Morgenstern Publisher: Collection: arXiv preprint arXiv:2201.02387 |
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| Abstract |
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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.
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