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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

plain_text

• Size of downloaded dataset files: 17.76 MB
• Size of the generated dataset: 73.55 MB
• Total amount of disk used: 91.31 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 Sample Size

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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

More Information Needed

Licensing Information

More Information Needed

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.