Syntactic Learnability of Echo State Neural Language Models at Scale

Ryo Ueda; Tatsuki Kuribayashi; Shunsuke Kando; Kentaro Inui

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arXiv · 2503.01724

Syntactic Learnability of Echo State Neural Language Models at Scale

Ryo Ueda·Tatsuki Kuribayashi·Shunsuke Kando·Kentaro Inui

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Abstract

What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability? To explore such a simple but sufficient neural language model, we revisit a basic reservoir computing (RC) model, Echo State Network (ESN), a restricted class of simple Recurrent Neural Networks. Our experiments showed that ESN with a large hidden state is comparable or superior to Transformer in grammaticality judgment tasks when trained with about 100M words, suggesting that architectures as complex as that of Transformer may not always be necessary for syntactic learning.

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Ryo Ueda, Tatsuki Kuribayashi, Shunsuke Kando, Kentaro Inui. 2025-03-03. Syntactic Learnability of Echo State Neural Language Models at Scale. https://arxiv.org/abs/2503.01724

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