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 crossgenre domain adaptation.
The Multi-Genre Natural Language Inference (MultiNLI) corpus REF uses data from ten distinct genres of English language for the the task of natural language inference (prediction of whether the relationship between two sentences represents entailment, contradiction, or neither).
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
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Computer Science