用强化学习模拟语言语法的演化，揭示表达需求如何塑造语言结构。
The Emergence of Grammar through Reinforcement Learning
- 基于消息概率设计分步强化学习算法，模拟语言习得过程。
- 数值模拟显示语言历史演变符合功能主义假设，表达需求驱动语法形成。
- 案例验证模型可解释英语历史中的语法变化，适合语言演化研究者。
本文通过强化学习理论的新应用，建模句法与语义组合系统的语法演化。为检验'说话者的表达目的塑造语言'的功能主义假说，模型引入特定语境下可表达信息的概率分布。所提出的学与产算法将语言学习分解为一系列简单步骤，每一步均利用消息概率进行优化。结果以语言历史的数值模拟和解析证明形式呈现。通过两个英语历史案例，展示了该数学模型在自然语言研究中的潜力。
原文摘要 · Abstract (English)
The evolution of grammatical systems of syntactic and semantic composition is modeled here with a novel application of reinforcement learning theory. To test the functionalist thesis that speakers' expressive purposes shape their language, we include within the model a probability distribution over different messages that could be expressed in a given context. The proposed learning and production algorithm then breaks down language learning into a sequence of simple steps, such that each step benefits from the message probabilities. The results are presented in the form of numerical simulations of language histories and analytic proofs. The potential for applying these mathematical models to the study of natural language is illustrated with two case studies from the history of English.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。