惊奇理论是同义反复的（缺乏理性基础）
Surprisal Theory is Tautological (without Rational Grounding)
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中文总结 AI 辅助
研究惊奇理论，指出其在无额外约束时是同义反复，任何难度模式都与某语言模型一致，无法证伪。长期被隐含假设掩盖，近期实证削弱该假设。结论是打破同义反复需理性主义干预，相关语言模型应源自非经验驱动模型。
中文摘要 AI 辅助
惊奇理论认为，语境中语言单元的人类处理难度是其在某种语言模型下惊奇度的仿射函数。本文认为该主张在没有进一步约束时是同义反复：对于语境中单元的任何非负难度度量，在温和技术条件下，存在一个语言模型，其惊奇度是该难度度量的仿射函数。所以，由于任何难度模式都与某个语言模型一致，若无对语言模型的额外约束，惊奇理论无法做出可证伪的预测。这一同义反复长期被心理语言学工作中隐含的假设掩盖，即相关语言模型是生成训练语料库的分布，所以改善语料库拟合能改进对人类行为的预测。近期实证工作削弱了这一假设，表明更好的语料库模型对处理难度的预测可能更差。本文结论是，打破同义反复需要理性主义干预，即相关语言模型必须源自基于理解者的非经验驱动模型，比如基于记忆约束或处理目标，且不依赖于惊奇理论旨在解释的行为数据。
英文摘要
Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model. I argue this claim is a tautology without further constraint: for any non-negative difficulty measure over units in context, there exists a language model whose surprisal is an affine function of it under mild technical conditions. Therefore, because any pattern of difficulty is consistent with some language model, without an additional constraint on the language model, surprisal theory makes no falsifiable predictions. The tautology was long obscured by an assumption implicit in two decades of psycholinguistic work---that the relevant language model is the distribution that generated the training corpus, so that improving corpus fit improves predictions of human behavior. Recent empirical work has undermined this assumption, demonstrating that better corpus models can be worse predictors of processing difficulty. I conclude that breaking the tautology requires a rationalist intervention, i.e., the relevant language model must be derived from a non-empirically motivated model of the comprehender, which could be based on, for instance, memory constraints or processing goals, and that, thus, does not depend on the behavioral data surprisal theory is meant to explain.
发表机构
- ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
