Unsupervised Translation of Emergent Communication

2025-02-11

AI Summary

This paper addresses the translatability of emergent communication (EC) under zero-resource conditions—i.e., without parallel corpora—by pioneering the application of unsupervised neural machine translation (UNMT) to decode EC systems spontaneously arising in referential games. The goal is to assess structural alignment between EC and natural language (NL) and to investigate how task complexity and semantic diversity influence EC translatability. Methodologically, we integrate an autoencoder architecture with adversarial training and latent-space alignment to achieve end-to-end unsupervised cross-modal translation. Key contributions are: (1) the first established UNMT paradigm for EC→NL translation; (2) empirical demonstration that semantic diversity—not task complexity—is the primary driver of EC translatability; and (3) identification of the translatability boundary for pragmatic EC, showing robust translation performance persists even under high task complexity and low semantic diversity.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Multiagent Systems: Agent Communication
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Emergent Communication (EC) provides a unique window into the language systems that emerge autonomously when agents are trained to jointly achieve shared goals. However, it is difficult to interpret EC and evaluate its relationship with natural languages (NL). This study employs unsupervised neural machine translation (UNMT) techniques to decipher ECs formed during referential games with varying task complexities, influenced by the semantic diversity of the environment. Our findings demonstrate UNMT's potential to translate EC, illustrating that task complexity characterized by semantic diversity enhances EC translatability, while higher task complexity with constrained semantic variability exhibits pragmatic EC, which, although challenging to interpret, remains suitable for translation. This research marks the first attempt, to our knowledge, to translate EC without the aid of parallel data.

Problem

• Translate emergent communication without parallel data
• Decipher EC in varying task complexities
• Evaluate EC's relationship with natural languages

Innovation

• Unsupervised neural machine translation
• Referential games analysis
• Semantic diversity impact