Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| license: | |
| - cc-by-nc-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 100K<n<1M | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - question-answering | |
| task_ids: | |
| - open-domain-qa | |
| paperswithcode_id: null | |
| pretty_name: Cryptonite | |
| dataset_info: | |
| - config_name: default | |
| features: | |
| - name: agent_info | |
| sequence: | |
| - name: Bottomline | |
| dtype: string | |
| - name: Role | |
| dtype: string | |
| - name: Target | |
| dtype: float32 | |
| - name: agent_turn | |
| sequence: int32 | |
| - name: dialogue_acts | |
| sequence: | |
| - name: intent | |
| dtype: string | |
| - name: price | |
| dtype: float32 | |
| - name: utterance | |
| sequence: string | |
| - name: items | |
| sequence: | |
| - name: Category | |
| dtype: string | |
| - name: Images | |
| dtype: string | |
| - name: Price | |
| dtype: float32 | |
| - name: Description | |
| dtype: string | |
| - name: Title | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 8538836 | |
| num_examples: 5247 | |
| - name: test | |
| num_bytes: 1353933 | |
| num_examples: 838 | |
| - name: validation | |
| num_bytes: 966032 | |
| num_examples: 597 | |
| download_size: 25373618 | |
| dataset_size: 10858801 | |
| - config_name: cryptonite | |
| features: | |
| - name: clue | |
| dtype: string | |
| - name: answer | |
| dtype: string | |
| - name: enumeration | |
| dtype: string | |
| - name: publisher | |
| dtype: string | |
| - name: date | |
| dtype: int64 | |
| - name: quick | |
| dtype: bool | |
| - name: id | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 52228597 | |
| num_examples: 470804 | |
| - name: validation | |
| num_bytes: 2901768 | |
| num_examples: 26156 | |
| - name: test | |
| num_bytes: 2908275 | |
| num_examples: 26157 | |
| download_size: 21615952 | |
| dataset_size: 58038640 | |
| config_names: | |
| - cryptonite | |
| - default | |
| # Dataset Card for Cryptonite | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [Github](https://github.com/aviaefrat/cryptonite) | |
| - **Repository:** [Github](https://github.com/aviaefrat/cryptonite) | |
| - **Paper:** [Arxiv](https://arxiv.org/pdf/2103.01242.pdf) | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Twitter](https://twitter.com/AviaEfrat) | |
| ### 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. | |
| ```python | |
| { | |
| '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](https://github.com/theo-m) for adding this dataset. |