Inherent Disagreements in Human Textual Inferences

We analyze human{'}s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation {``}noise{''}, but rather persist as we collect more ratings and as we vary the amount of context provided to raters... (read more)

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