  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.
