Emergence of Compositional Language with Deep Generational Transmission

TL;DR

This paper demonstrates that introducing cultural transmission dynamics in deep reinforcement learning agents enhances the emergence of compositional language, leading to better generalization of communication.

Contribution

It introduces a novel method of simulating cultural transmission through periodic agent replacement to promote compositional language emergence.

Findings

Cultural transmission improves compositional generalization.

Periodic agent replacement fosters language structure.

Languages exhibit better generalization with transmission dynamics.

Abstract

Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional -- i.e., express meaning by combining words which themselves have meaning. Evolutionary linguists have found that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization.