NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use

Underline Science Inc.

Abstract

Lexical semantic change has been investigated extensively through corpus studies across many languages, but its underlying principles remain hard to unravel through experimental paradigms due to their extended diachronic processes. This work introduces NeLLCom-Lex, a neural-agent framework to simulate semantic change by first grounding agents in a real lexical system (e.g. English) and then systematically manipulating their communicative needs. We use a well-established color naming task to study which factors lead agents to modulate their naming choices according to the situational context and maintain efficient and understandable lexical systems. Our experiments with different supervised and reinforcement learning pipelines show that neural agents trained to `speak' an existing language can reproduce human-like patterns in color naming, supporting their use for simulating and explaining semantic change in real lexical systems.

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