We introduce contextual explanation networks (CENs)-a class of models that learn to predict by generating and leveraging intermediate explanations. CENs combine deep networks with context-specific probabilistic models and construct explanations in the form of locally-correct hypotheses. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs can easily match or outperform the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.
In another approach, Al-Shedivat et al. REF proposed Contextual Explanation Networks (CENs), a class of models that jointly learns to predict and explain its decision.
Contextual Explanation Networks
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