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
@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",
}
anli
: Evaluatesanli_r1
,anli_r2
, andanli_r3
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.
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?