Dataset Card for "anli"

Dataset Summary

The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset, The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure. ANLI is much more difficult than its predecessors including SNLI and MNLI. It contains three rounds. Each round has train/dev/test splits.

Supported Tasks and Leaderboards

More Information Needed

Languages

English

Dataset Structure

Data Instances

plain_text

• Size of downloaded dataset files: 18.62 MB
• Size of the generated dataset: 77.12 MB
• Total amount of disk used: 95.75 MB

An example of 'train_r2' looks as follows.

This example was too long and was cropped:

{
    "hypothesis": "Idris Sultan was born in the first month of the year preceding 1994.",
    "label": 0,
    "premise": "\"Idris Sultan (born January 1993) is a Tanzanian Actor and comedian, actor and radio host who won the Big Brother Africa-Hotshot...",
    "reason": "",
    "uid": "ed5c37ab-77c5-4dbc-ba75-8fd617b19712"
}

Data Fields

The data fields are the same among all splits.

plain_text

• uid : a string feature.
• premise : a string feature.
• hypothesis : a string feature.
• label : a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
• reason : a string feature.

Data Splits

name	train_r1	dev_r1	train_r2	dev_r2	train_r3	dev_r3	test_r1	test_r2	test_r3
plain_text	16946	1000	45460	1000	100459	1200	1000	1000	1200

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

More Information Needed

Licensing Information

cc-4 Attribution-NonCommercial

Citation Information

@InProceedings{nie2019adversarial,
    title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
    author={Nie, Yixin
                and Williams, Adina
                and Dinan, Emily
                and Bansal, Mohit
                and Weston, Jason
                and Kiela, Douwe},
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    publisher = "Association for Computational Linguistics",
}

Contributions

Thanks to @thomwolf , @easonnie , @lhoestq , @patrickvonplaten for adding this dataset.