探究人类语言解析为何非随机，发现复杂结构与语义预期可抑制随机策略
Why Are Parsing Actions for Understanding Message Hierarchies Not Random?
- 引入复杂层级输入与突现性惩罚项，模拟真实语言理解场景
- 随机解析策略在新设定下通信准确率显著下降，验证非随机性必要性
- 适合对语言认知机制、神经符号系统感兴趣的读者
若人类理解语言依赖随机解析动作，则需构建能应对任意层级结构的鲁棒符号系统。然而，人类解析策略显然不遵循随机模式。此前研究发现，具有层级偏见的模型采用随机解析策略仍可实现高通信准确率。本研究通过两项改进重新检验此现象：(I) 使用更复杂的、具层级结构的输入，使随机解析更难进行语义解读；(II) 在目标函数中引入与突现性相关的项，该因素已被证实影响自然语言中词序与字序。结果表明，在新设定下，采用随机解析策略的智能体通信准确率大幅下降，说明真实语言理解中的非随机性具有必要性。
原文摘要 · Abstract (English)
If humans understood language by randomly selecting parsing actions, it might have been necessary to construct a robust symbolic system capable of being interpreted under any hierarchical structure. However, human parsing strategies do not seem to follow such a random pattern. Why is that the case? In fact, a previous study on emergent communication using models with hierarchical biases have reported that agents adopting random parsing strategies$\unicode{x2013}$ones that deviate significantly from human language comprehension$\unicode{x2013}$can achieve high communication accuracy. In this study, we investigate this issue by making two simple and natural modifications to the experimental setup: (I) we use more complex inputs that have hierarchical structures, such that random parsing makes semantic interpretation more difficult, and (II) we incorporate a surprisal-related term, which is known to influence the order of words and characters in natural language, into the objective function. With these changes, we evaluate whether agents employing random parsing strategies still maintain high communication accuracy.
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