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Understanding entailment and contradiction is fundamental to understandingnatural language, and inference about entailment and contradiction is avaluable testing ground for the development of semantic representations.However, machine learning research in this area has been dramatically limitedby the lack of large-scale resources. To address this, we introduce theStanford Natural Language Inference corpus, a new, freely available collectionof labeled sentence pairs, written by humans doing a novel grounded task basedon image captioning. At 570K pairs, it is two orders of magnitude larger thanall other resources of its type. This increase in scale allows lexicalizedclassifiers to outperform some sophisticated existing entailment models, and itallows a neural network-based model to perform competitively on naturallanguage inference benchmarks for the first time.

A large annotated corpus for learning natural language inference