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While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency as a generalization of prediction error. We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency. Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign. Wedoi:10.18653/v1/d19-1405 dblp:conf/emnlp/LiGMS19 fatcat:l3q532dlmnapvo4o6mg2vcfn5a