Could the Road to Grounded, Neuro-symbolic AI be Paved with Words-as-Classifiers?
This addresses the challenge of integrating different semantic approaches for AI systems, but it appears incremental as it builds on existing models.
The paper argues that the words-as-classifiers model could unify formal, distributional, and grounded theories of computational semantics, based on a review of literature and a small experiment.
Formal, Distributional, and Grounded theories of computational semantics each have their uses and their drawbacks. There has been a shift to ground models of language by adding visual knowledge, and there has been a call to enrich models of language with symbolic methods to gain the benefits from formal, distributional, and grounded theories. In this paper, we attempt to make the case that one potential path forward in unifying all three semantic fields is paved with the words-as-classifier model, a model of word-level grounded semantics that has been incorporated into formalisms and distributional language models in the literature, and it has been well-tested within interactive dialogue settings. We review that literature, motivate the words-as-classifiers model with an appeal to recent work in cognitive science, and describe a small experiment. Finally, we sketch a model of semantics unified through words-as-classifiers.