Emergent Language: A Survey and Taxonomy

TL;DR

This survey comprehensively reviews 181 publications on emergent language in AI, analyzing terminology, evaluation methods, and research gaps to serve as a key reference for future research in the field.

Contribution

It provides the first extensive taxonomy and analysis of emergent language research, highlighting key concepts, evaluation criteria, and identifying gaps in current knowledge.

Findings

Established a comprehensive terminology framework

Analyzed evaluation metrics and methods used in the field

Identified critical research gaps and future directions

Abstract

The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.