  Parallel neurosymbolic architectures have been applied effectively in NLP by
distilling knowledge from a logic theory into a deep model.However, prior art
faces several limitations including supporting restricted forms of logic
theories and relying on the assumption of independence between the logic and
the deep network. We present Concordia, a framework overcoming the limitations
of prior art. Concordia is agnostic both to the deep network and the logic
theory offering support for a wide range of probabilistic theories. Our
framework can support supervised training of both components and unsupervised
training of the neural component. Concordia has been successfully applied to
tasks beyond NLP and data classification, improving the accuracy of
state-of-the-art on collective activity detection, entity linking and
recommendation tasks.
