重新审视语境对阅读时间的影响，发现过去高估了语境的作用。
On the Role of Context in Reading Time Prediction
- 用正交化方法分离语境与词频影响，得到更纯净的语境指标。
- 新指标显示语境对阅读时间的解释力显著下降。
- 适合关注语言认知模型可解释性的研究者阅读。
我们从新视角探讨读者在实时语言理解中如何整合语境。基于意外度理论，语言单位（如单词）的处理努力是其上下文信息量的仿射函数。我们发现，意外度只是语言模型生成上下文预测的众多方式之一；另一种是单位与其上下文间的点互信息（PMI），当控制单字频率时，其预测能力与意外度相当。但两者均与词频相关，说明它们无法单独反映语境。为此，我们提出将意外度投影到词频的正交补空间，得到一个与词频无关的新语境指标。实验表明，使用该正交化指标时，语境对阅读时间方差的解释比例大幅降低。从可解释性角度，这表明以往研究可能夸大了语境在预测阅读时间中的作用。
原文摘要 · Abstract (English)
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.
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