The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a fully parallel parser: words consistent with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden-path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden-path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word-by-word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self-paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden-path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability.

中文翻译：

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单阶段预测模型无法解释句法消歧难度的大小

消歧句法上有歧义的句子有利于不太优选的解析会导致消歧点处的阅读速度变慢。这种被称为花园路径效应的现象产生了一些模型，其中读者最初只维护句子可能解析的一个子集，随后需要耗时的重新分析来重建丢弃的解析。最近的一项提议认为，花园路径效应可以减少到完全并行解析器中出现的令人惊讶的情况：与最初不受欢迎但最终正确解析一致的词比与不正确解析一致的词更难预测。由于可预测性在阅读远远超出花园小径句子的范围内具有普遍影响，因此这种无需再分析机制的说明更加简洁。至关重要的是，它预测了 surprisal 的线性效应：花园路径效应预计与最终正确解释和最终错误解释之间的单词 surprisal 差异成正比。为了测试这个预测，我们使用循环神经网络语言模型来估计三个暂时不明确的结构的逐字惊奇。然后，我们估计了由于人类自定进度阅读时间的每一点惊奇所导致的放缓，并使用该数量来预测句法消歧难度。Surprisal 成功预测了花园小径效应的存在，但严重低估了它们的大小，并且未能预测它们在整个建筑中的相对严重程度。我们得出结论，句法消歧困难的完整解释可能需要超出可预测性的恢复机制。