论上下文在阅读时间预测中的作用
On the Role of Context in Reading Time Prediction
AI总结：
本文提出将surprisal投影到频率的正交补上构建与频率不相关的上下文预测器，发现上下文对阅读时间方差的解释比例显著减小，表明以往研究高估了上下文的作用。
摘要：
我们提出了关于读者在实时语言理解过程中如何整合上下文的新视角。我们的建议基于surprisal理论，该理论假设语言单元（如单词）的处理努力是其上下文信息内容的仿射函数。我们首先观察到，surprisal只是从语言模型中导出上下文预测器的众多潜在方式之一。另一种是单元与其上下文之间的点互信息（PMI），在控制unigram频率时，其预测能力与surprisal相同。此外，PMI和surprisal都与频率相关，这意味着两者都不包含仅关于上下文的信息。为此，我们提出一种技术，将surprisal投影到频率的正交补上，得到与频率不相关的新上下文预测器。实验表明，当用正交化预测器表示上下文时，上下文解释的阅读时间方差比例显著减小。从可解释性角度看，这表明以往研究可能夸大了上下文在预测阅读时间中的作用。
英文摘要：
We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit (e.g., a word) is an affine function of its in-context information content. We first observe that surprisal is only one out of many potential ways that a contextual predictor can be derived from a language model. Another one is the pointwise mutual information (PMI) between a unit and its context, which turns out to yield the same predictive power as surprisal when controlling for unigram frequency. Moreover, both PMI and surprisal are correlated with frequency. This means that neither PMI nor surprisal contains information about context alone. In response to this, we propose a technique where we project surprisal onto the orthogonal complement of frequency, yielding a new contextual predictor that is uncorrelated with frequency. Our experiments show that the proportion of variance in reading times explained by context is a lot smaller when context is represented by the orthogonalized predictor. From an interpretability standpoint, this indicates that previous studies may have overstated the role that context has in predicting reading times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
