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Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language (2103.01242v2)

Published 1 Mar 2021 in cs.CL, cs.AI, cs.LG, and stat.ML

Abstract: Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease. We present Cryptonite, a large-scale dataset based on cryptic crosswords, which is both linguistically complex and naturally sourced. Each example in Cryptonite is a cryptic clue, a short phrase or sentence with a misleading surface reading, whose solving requires disambiguating semantic, syntactic, and phonetic wordplays, as well as world knowledge. Cryptic clues pose a challenge even for experienced solvers, though top-tier experts can solve them with almost 100% accuracy. Cryptonite is a challenging task for current models; fine-tuning T5-Large on 470k cryptic clues achieves only 7.6% accuracy, on par with the accuracy of a rule-based clue solver (8.6%).

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Authors (4)
  1. Avia Efrat (9 papers)
  2. Uri Shaham (35 papers)
  3. Dan Kilman (2 papers)
  4. Omer Levy (70 papers)
Citations (16)
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