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Human reasoning can often be understood as an interplay between two systems:the intuitive and associative ("System 1") and the deliberative and logical("System 2"). Neural sequence models -- which have been increasingly successfulat performing complex, structured tasks -- exhibit the advantages and failuremodes of System 1: they are fast and learn patterns from data, but are ofteninconsistent and incoherent. In this work, we seek a lightweight, training-freemeans of improving existing System 1-like sequence models by adding System2-inspired logical reasoning. We explore several variations on this theme inwhich candidate generations from a neural sequence model are examined forlogical consistency by a symbolic reasoning module, which can either accept orreject the generations. Our approach uses neural inference to mediate betweenthe neural System 1 and the logical System 2. Results in robust storygeneration and grounded instruction-following show that this approach canincrease the coherence and accuracy of neurally-based generations.

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning