README.md

ANLI

Paper

Title: Adversarial NLI: A New Benchmark for Natural Language Understanding

Paper Link: https://arxiv.org/abs/1910.14599

Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial human-and-model-in-the-loop procedure. It consists of three rounds that progressively increase in difficulty and complexity, and each question-answer includes annotator- provided explanations.

Homepage: https://github.com/facebookresearch/anli

Citation

@inproceedings{nie-etal-2020-adversarial,
    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",
}

Groups and Tasks

Groups

• anli: Evaluates anli_r1, anli_r2, and anli_r3

Tasks

• anli_r1: The data collected adversarially in the first round.
• anli_r2: The data collected adversarially in the second round, after training on the previous round's data.
• anli_r3: The data collected adversarially in the third round, after training on the previous multiple rounds of data.

Checklist

For adding novel benchmarks/datasets to the library:

• Is the task an existing benchmark in the literature?
• Have you referenced the original paper that introduced the task?
• If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?

If other tasks on this dataset are already supported:

• Is the "Main" variant of this task clearly denoted?
• Have you provided a short sentence in a README on what each new variant adds / evaluates?
• Have you noted which, if any, published evaluation setups are matched by this variant?