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Explainable Neural Computation via Stack Neural Module Networks
2021
Applied AI Letters
In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and prediction. Existing models designed to produce interpretable traces of their decision-making process typically require these traces to be supervised at training time. In this paper, we present a novel neural
doi:10.1002/ail2.39
fatcat:o54xfuyapze75f545qznkn5ng4