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A Review of Winograd Schema Challenge Datasets and Approaches

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arxiv 2004.13831 v1 pith:YT75W2UR submitted 2020-04-23 cs.CL cs.AI

classification cs.CLcs.AI
keywords challengeschemawinogradapproachescommonsensedatasetsresolvedsentences
verification ladder T0 review T1 audit T2 compute T3 formal
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The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multilingual Test-Time Scaling via Initial Thought Transfer

    cs.CL 2025-05 reject novelty 5.0 of 10

    MITT, a prefix-tuning method for multilingual test-time scaling, is evaluated on questions whose English reasoning was used for training, confounding the reported gains.

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