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具人类特征。然而，现有文献对层级模型的解析策略并未明确区分。本文旨在探究层级结构是否使语言模型更贴近人类认知，以及何种解析策略最具认知合理性。为此，我们针对日语中带有尾核心左分支结构的数据，将长短期记忆网络（LSTM）作为序列模型，将采用自上而下与左角解析策略的循环神经网络文法（RNNGs）作为层级模型，评估了三类模型对人类阅读时间的拟合效果。计算建模结果表明，左角RNNGs的性能优于自上而下RNNGs和LSTM，显示层级结构结合左角架构比自上而下或序列架构更具认知合理性。此外，本文还将讨论认知合理性与（i）困惑度、（ii）解析策略及（iii）束搜索大小之间的关系。