Improving Coherence And Consistency In Neural Sequence Models With Dual-system, Neuro-symbolic Reasoning

Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake. Arxiv 2021 – 30 citations

[Paper]    
Training Techniques

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 successful at performing complex, structured tasks – exhibit the advantages and failure modes of System 1: they are fast and learn patterns from data, but are often inconsistent and incoherent. In this work, we seek a lightweight, training-free means of improving existing System 1-like sequence models by adding System 2-inspired logical reasoning. We explore several variations on this theme in which candidate generations from a neural sequence model are examined for logical consistency by a symbolic reasoning module, which can either accept or reject the generations. Our approach uses neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.

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