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Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorizationdoi:10.18653/v1/n18-2017 dblp:conf/naacl/GururanganSLSBS18 fatcat:up67r2jwmnawrdsnigw3nuc6h4