用神经网络代理模拟语言演化，发现交流促成了语法标记分化。
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents
- 用神经网络代理先学语言再交流，模拟语言演化过程。
- 仅学习不产生标记分化，交流后才出现差异性标记。
- 验证了沟通在语法演化中的关键作用，适合语言演化研究者。
差异性格标记（Differential Case Marking, DCM）指语法格标记根据语义、语用等不同因素选择性使用。已有研究表明，人类参与的人工语言学习实验中，沟通在塑造DCM中起关键作用（Smith & Culbertson, 2020）。本研究采用基于神经网络的多智能体强化学习框架，让代理先习得人工语言，再进行交际互动，从而与人类实验直接对比。使用通用通信优化算法和无语言或语义偏好先验的神经网络学习器，结果表明：仅通过学习无法产生DCM，但当代理之间交流时，差异性标记自然浮现。这支持了先前研究结论，即沟通在语言演化中至关重要，并展示了神经代理模型对语言演化实验研究的补充潜力。
原文摘要 · Abstract (English)
Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith & Culbertson, 2020). Multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human result. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith and Culbertson (2020)'s findings that highlight the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.
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