@inproceedings{efrat-etal-2021-cryptonite,
title = "Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language",
author = "Efrat, Avia and
Shaham, Uri and
Kilman, Dan and
Levy, Omer",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.344/",
doi = "10.18653/v1/2021.emnlp-main.344",
pages = "4186--4192",
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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<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%).</abstract>
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%0 Conference Proceedings
%T Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
%A Efrat, Avia
%A Shaham, Uri
%A Kilman, Dan
%A Levy, Omer
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F efrat-etal-2021-cryptonite
%X 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%).
%R 10.18653/v1/2021.emnlp-main.344
%U https://aclanthology.org/2021.emnlp-main.344/
%U https://doi.org/10.18653/v1/2021.emnlp-main.344
%P 4186-4192
Markdown (Informal)
[Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language](https://aclanthology.org/2021.emnlp-main.344/) (Efrat et al., EMNLP 2021)
ACL