Task Categories: question-answering
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M 1K<n<10K
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original
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Dataset Card for Cryptonite

Dataset Summary

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%).



Dataset Structure

Data Instances

This is one example from the train set.

  'clue': 'make progress socially in stated region (5)',
  'answer': 'climb',
  'date': 971654400000,
  'enumeration': '(5)',
  'id': 'Times-31523-6across',
  'publisher': 'Times',
  'quick': False

Data Fields

  • clue: a string representing the clue provided for the crossword
  • answer: a string representing the answer to the clue
  • enumeration: a string representing the
  • publisher: a string representing the publisher of the crossword
  • date: a int64 representing the UNIX timestamp of the date of publication of the crossword
  • quick: a bool representing whether the crossword is quick (a crossword aimed at beginners, easier to solve)
  • id: a string to uniquely identify a given example in the dataset

Data Splits

Train (470,804 examples), validation (26,156 examples), test (26,157 examples).

Dataset Creation

Curation Rationale

Crosswords from the Times and the Telegraph.

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Avia Efrat, Uri Shaham, Dan Kilman, Omer Levy

Licensing Information


Citation Information

      title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, 
      author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},


Thanks to @theo-m for adding this dataset.

Update on GitHub