提出新框架，更精准预测语言处理中的预期与反应行为。
Generalized Measures of Anticipation and Responsivity in Online Language Processing
- 基于蒙特卡洛模拟生成语言延续，定义可扩展的预测机制
- 新指标在人类补全概率、脑电波响应上优于传统意外度
- 适合研究语言认知、神经语言学及模型可解释性的人群
我们提出一种在线语言处理中预测不确定性信息论度量的推广框架，基于增量语境的预期延续模拟。该框架为预期性和响应性度量提供形式化定义，并赋予实验者构建超越标准下一符号熵和意外度的新表达式的能力。尽管从语言模型提取这些标准量便捷，但使用蒙特卡洛模拟估算替代的响应性和预期性度量在实证上表现更优：新公式特例在预测人类补全概率、ELAN、LAN和N400振幅方面优于意外度，且与意外度在预测阅读时间上具有更强互补性。
原文摘要 · Abstract (English)
We introduce a generalization of classic information-theoretic measures of predictive uncertainty in online language processing, based on the simulation of expected continuations of incremental linguistic contexts. Our framework provides a formal definition of anticipatory and responsive measures, and it equips experimenters with the tools to define new, more expressive measures beyond standard next-symbol entropy and surprisal. While extracting these standard quantities from language models is convenient, we demonstrate that using Monte Carlo simulation to estimate alternative responsive and anticipatory measures pays off empirically: New special cases of our generalized formula exhibit enhanced predictive power compared to surprisal for human cloze completion probability as well as ELAN, LAN, and N400 amplitudes, and greater complementarity with surprisal in predicting reading times.
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