Neural-Symbolic Integration: A Compositional Perspective

Despite significant progress in the development of neuralsymbolic frameworks, the question of how to integrate a neural and a symbolic system in a compositional manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their internal structure and semantics. Instead, we expect only that each module exposes certain methods for accessing the functions that the module implements: the symbolic module exposes a deduction method for computing the function’s output on a given input, and an abduction method for computing the function’s inputs for a given output; the neural module exposes a deduction method for computing the function’s output on a given input, and an induction method for updating the function given input-output training instances. We are, then, able to show that a symbolic module — with any choice for syntax and semantics, as long as the deduction and abduction methods are exposed — can be cleanly integrated with a neural module, and facilitate the latter’s efficient training, achieving empirical performance that exceeds that of previous work1.

Authors

• Efthymia Tsamoura Samsung AI Research
• Timothy Hospedales University of Edinburgh Samsung AI Center, Cambridge, UK
• Loizos Michael

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