神经代理在交互中因处理压力自发出现依赖距离最小化现象。
Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
- 用循环神经网络模拟语言学习与交流，研究认知限制对词序的影响。
- 仅在增量加工压力下，代理才稳定表现出依赖距离最小化偏好。
- 结果揭示人类语言偏好可能源于信息处理的生理约束，适合语言演化研究者。
在多种语法选择中，语言使用者倾向于采用能减少句法依赖长度的词序，这一原则称为依赖距离最小化（DLM）。其起源仍是未解之谜，尤其是否源于高效信息处理的约束。计算模拟为探究语言现象的成因提供了有力手段，但以往关于DLM的模拟未考虑真实交互情境，结果不一。本研究利用基于循环神经网络（RNN）的新型语言学习与通信框架，在人工语言中考察了多个与处理限制相关的因素对DLM出现的影响，包括听觉噪声、说话能力有限及增量句子处理。结果表明，这些因素在塑造神经代理的词序偏好上存在复杂交互：在完整语义空间中，代理趋于单一主导词序；在半语义空间中，呈现“短在前、长在后”的偏好，仅在动词前置语言中与DLM一致。唯有在增量处理压力下，才能稳定出现一致的DLM偏好。这表明人类认知处理限制可能确实在塑造DLM中发挥作用。研究为神经模型再现人类语言偏好提供了条件，并指出了设计能捕捉人类语言处理偏好的涌现通信模型的挑战。
原文摘要 · Abstract (English)
Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length minimization (DLM). The origins of this preference remain an open question, particularly whether it originates from constraints on efficient information processing. Computational simulations provide a powerful approach to identifying the factors influencing the emergence of linguistic phenomena. However, previous simulations of DLM have not examined realistic interaction contexts and have produced mixed results. The present study investigates the emergence of DLM in artificial languages using a recently proposed language learning and communication framework based on recurrent neural networks (RNNs). In this framework, agents are trained to speak and interpret artificial languages and then use these languages to communicate. Using this framework, we study the impact of several factors related to processing limitations in a communicative setting, such as noise during listening, limited speaker capacity, and incremental sentence processing. Our results reveal a complex interplay among these factors in shaping word order preferences in neural agents. Specifically, in the full meaning space, agents regularize toward a single dominant word order, while in the half meaning space they show a short-before-long preference that only aligns with DLM in verb-initial languages. A consistent DLM preference emerges only when agents are subject to incremental processing pressure. These findings suggest that limitations in human cognitive processing may indeed play a role in shaping DLM. Our findings provide insights into the conditions under which neural models replicate human-like preferences and highlight the challenges of designing emergent communication models that capture human cognitive biases in language processing.
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。