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A Broad-coverage Challenge Corpus For Sentence Understanding Through Inference

Adina Williams, Nikita Nangia, Samuel R. Bowman . Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) 2018 – 3365 citations

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ACL Datasets Evaluation Fine Tuning NAACL

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora available for the task of NLI, at 433k examples, this corpus improves upon available resources in its coverage: it offers data from ten distinct genres of written and spoken English–making it possible to evaluate systems on nearly the full complexity of the language–and it offers an explicit setting for the evaluation of cross-genre domain adaptation.

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