README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
languages:
  - en
licenses:
  - cc-by-nc-4-0
multilinguality:
  - monolingual
size_categories:
  cryptonite:
    - 100K<n<1M
  default:
    - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - open-domain-qa

Dataset Card for Cryptonite

Table of Contents

• Dataset Card for Cryptonite
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: Github
• Repository: Github
• Paper: Arxiv
• Leaderboard:
• Point of Contact: Twitter

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%).

Languages

English

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]

Annotations

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

cc-by-nc-4.0

Citation Information

@misc{efrat2021cryptonite,
      title={Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language}, 
      author={Avia Efrat and Uri Shaham and Dan Kilman and Omer Levy},
      year={2021},
      eprint={2103.01242},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @theo-m for adding this dataset.