A Holistic Approach to Compositional Semantics: a connectionist model and robot experiments

Abstract

We present a novel connectionist model for acquiring the semantics of language through the behavioral experiences of a real robot. We focus on the “compositionality ” of semantics, which is a fundamental characteristic of human language, namely, the fact that we can understand the meaning of a sentence as a combination of the meanings of words. The essential claim is that a compositional semantic representation can be self-organized by generalizing correspondences between sentences and behavioral patterns. This claim is examined and confirmed through simple experiments in which a robot generates corresponding behaviors from unlearned sentences by analogy with the correspondences between learned sentences and behaviors. 1