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WinoReg: A New Faster and More Accurate Metric of Hardness for Winograd Schemas

Authors: Nicos Isaak; Loizos Michael;

WinoReg: A New Faster and More Accurate Metric of Hardness for Winograd Schemas

Abstract

The Winograd Schema Challenge (WSC), the task of resolving pronouns in certain carefully-structured sentences, has received considerable interest in the past few years as an alternative to the Turing Test. In our recent work we demonstrated the plausibility of using commonsense knowledge, automatically acquired from raw text in English Wikipedia, towards computing a metric of hardness for a limited number of Winograd Schemas.In this work we present WinoReg, a new system to compute hardness of Winograd Schemas, by training a Random Forest classifier over a rich set of features identified in relevant WSC works in the literature. Our empirical study shows that this new system is considerably faster and more accurate compared to the system proposed in our earlier work, making its use as part of other WSC-based systems feasible.

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Microsoft Academic Graph classification: Theoretical computer science Computer science Metric (mathematics)

23 references, page 1 of 3

[1] Dan Bailey, Amelia Harrison, Yuliya Lierler, Vladimir Lifschitz, and Julian Michael. The Winograd Schema Challenge and Reasoning about Correlation. In Working Notes of the Symposium on Logical Formalizations of Commonsense Reasoning, 2015.

[2] Collin F Baker, Charles J Fillmore, and John B Lowe. The Berkeley Framenet Project. In Proceedings of the 17th international conference on Computational linguistics-Volume 1, pages 86{90. Association for Computational Linguistics, 1998.

[3] David Bender. Establishing a Human Baseline for the Winograd Schema Challenge. In MAICS, pages 39{45, 2015.

[4] Tejas Ulhas Budukh. An Intelligent Co-Reference Resolver for WInograd Schema Sentences Containing Resolved Semantic Entities, 2013.

[5] Nathanael Chambers and Dan Jurafsky. Unsupervised Learning of Narrative Event Chains. In Proceedings of ACL-08: HLT, pages 789{797, 2008.

[6] Hannah Fry. Hello World: How to be Human in the Age of the Machine. Random House, 2018.

[7] Nicos Isaak and Loizos Michael. Tackling the Winograd Schema Challenge Through Machine Logical Inferences. In David Pearce and Helena So a Pinto, editors, STAIRS, volume 284 of Frontiers in Arti cial Intelligence and Applications, pages 75{86. IOS Press, 2016.

[8] Nicos Isaak and Loizos Michael. A Data-Driven Metric of Hardness for WSC Sentences. In Daniel Lee, Alexander Steen, and Toby Walsh, editors, GCAI-2018. 4th Global Conference on Arti cial Intelligence, volume 55 of EPiC Series in Computing, pages 107{120. EasyChair, 2018. [OpenAIRE]

[9] Nicos Isaak and Loizos Michael. Using the Winograd Schema Challenge as a CAPTCHA. In Daniel Lee, Alexander Steen, and Toby Walsh, editors, GCAI-2018. 4th Global Conference on Arti cial Intelligence, volume 55 of EPiC Series in Computing, pages 93{106. EasyChair, 2018. [OpenAIRE]

[10] Nicos Isaak and Loizos Michael. WinoFlexi: A Crowdsourcing Platform for the Development of Winograd Schemas. In Jixue Liu and James Bailey, editors, AI 2019: Advances in Arti cial Intelligence, pages 289{302, Cham, 2019. Springer International Publishing.

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