In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.

翻译：在计算语言学中，层级结构已被证明能使语言模型更接近人类认知特性。然而，现有文献对层级模型的具体解析策略尚未达成共识。本文旨在探究层级结构是否确实能提升语言模型的人类相似性，以及何种解析策略最具认知合理性。为解决此问题，我们以日语中具有中心语后置左分支结构的语句为对象，对三种语言模型与人类阅读时间进行了对比评估：作为序列模型的LSTM，以及分别采用自顶向下和左角解析策略的循环神经网络文法（作为层级模型）。计算建模结果表明，左角RNNGs的性能优于自顶向下RNNGs和LSTM，说明层级结构与左角解析架构比自顶向下或序列架构更具认知合理性。此外，本文还将探讨认知合理性与（i）困惑度，（ii）解析过程，（iii）束搜索大小之间的关系。