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We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will growdoi:10.18653/v1/w18-5441 dblp:conf/emnlp/PoliakHRHPWD18a fatcat:jgh6i4foxrdajbzjndcojer7mi