用神经代理模拟词汇演变，揭示语言使用如何影响词义变化。
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
- 构建神经代理框架，基于真实词汇系统模拟语言演化
- 训练代理在颜色命名任务中复现人类的命名模式
- 验证通信需求驱动词义变化，适合语言学与AI交叉研究
词汇语义演变通常通过观察或实验方法研究；但观察方法（如语料分析、分布语义建模）难以揭示因果机制，而基于人类的实验范式因历时过程过长难以应用。本文提出NeLLCom-Lex，一种神经代理框架，通过将代理嵌入真实词汇系统（如英语），并系统性地操纵其沟通需求来模拟语义演变。以经典的颜色命名任务为例，我们模拟了单代内的词汇系统演化，探究哪些因素促使代理：(i) 发展出类人命名行为和词库，(ii) 根据沟通需求调整其行为与词汇。不同监督学习与强化学习路径的实验表明，经过训练以‘说’现有语言的神经代理，能高度复现人类在颜色命名中的模式，支持进一步利用NeLLCom-Lex揭示语义演变的内在机制。
原文摘要 · Abstract (English)
Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. English) and then systematically manipulating their communicative needs. Using a well-established color naming task, we simulate the evolution of a lexical system within a single generation, and study which factors lead agents to: (i) develop human-like naming behavior and lexicons, and (ii) change their behavior and lexicons according to their communicative needs. 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 to a remarkable extent, supporting the further use of NeLLCom-Lex to elucidate the mechanisms of semantic change.
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