Position Paper: Generalized grammar rules and structure-based generalization beyond classical equivariance for lexical tasks and transduction

Compositional generalization is one of the main properties which differentiates lexical learning in humans from state-of-art neural networks. We propose a general framework for building models that can generalize compositionally using the concept of Generalized Grammar Rules (GGRs), a class of symmetry-based compositional constraints for transduction tasks, which we view as a transduction analogue of equivariance constraints in physics-inspired tasks. Besides formalizing generalized notions of symmetry for language transduction, our framework is general enough to contain many existing works as …

art beyond building class concept constraints cs.cl cs.lg framework general generalized grammar humans networks neural networks paper rules state stat.ml symmetry tasks