神经智能体模拟中依存长度最小化涌现的影响因素
Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations
- University of Groningen（格罗宁根大学）
- Leiden University（莱顿大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究利用基于循环神经网络的语言学习与交流框架，模拟人工语言中依存长度最小化的涌现，发现增量处理压力是产生一致DLM偏好的关键因素，揭示了认知处理限制在塑造词序偏好中的作用。
中文摘要 AI 辅助
面对各种语法选项，语言使用者倾向于选择能够减少句法依存总体长度的词序选择，这一原则被称为依存长度最小化（DLM）。这种偏好的起源仍然是一个悬而未决的问题，特别是它是否源于高效信息处理的约束。计算模拟为识别影响语言现象涌现的因素提供了一种强有力的方法。然而，以往关于DLM的模拟并未考察现实互动情境，且产生了不一致的结果。本研究利用一个近期提出的基于循环神经网络（RNNs）的语言学习与交流框架，考察人工语言中DLM的涌现。在该框架中，智能体被训练去说和理解人工语言，然后使用这些语言进行交流。利用该框架，我们研究了与交流情境中处理限制相关的若干因素的影响，例如听音过程中的噪声、说话者能力有限以及增量式句子处理。我们的结果揭示了这些因素在塑造神经智能体词序偏好方面的复杂交互作用。具体而言，在完整意义空间中，智能体向单一主导词序规范化，而在半意义空间中，它们表现出短前长后的偏好，该偏好仅在动词居首语言中与DLM一致。只有当智能体受到增量处理压力时，才出现一致的DLM偏好。这些发现表明，人类认知处理的限制确实可能在塑造DLM中发挥作用。我们的发现为神经模型在何种条件下复现人类类似偏好提供了洞见，并强调了设计能够捕捉语言处理中人类认知偏差的涌现交流模型所面临的挑战。
英文摘要
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.
