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"description": "The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,\nThe dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.\nANLI is much more difficult than its predecessors including SNLI and MNLI.\nIt contains three rounds. Each round has train/dev/test splits.\n",
"citation": "@InProceedings{nie2019adversarial,\n title={Adversarial NLI: A New Benchmark for Natural Language Understanding},\n author={Nie, Yixin\n and Williams, Adina\n and Dinan, Emily\n and Bansal, Mohit\n and Weston, Jason\n and Kiela, Douwe},\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n year = \"2020\",\n publisher = \"Association for Computational Linguistics\",\n}\n",
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