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Learning Argument Structures with Recurrent Neural Network Grammars
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars ...
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Abstract:
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) ... : Accepted by EMNLP 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2109.04939
https://dx.doi.org/10.48550/arxiv.2109.04939
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Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars ...
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