A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

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.Comment: 10 pages, 1 figures, 5 tables. v2 corrects a misreported accuracy number for the CBOW model in the 'matched' setting. v3 adds a discussion of the difficulty of the corpus to the analysis section. v4 is the version that was accepted to NAACL201