Syntactic Learnability of Echo State Neural Language Models at Scale
Computation and Language
2025-03-04 v1
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.
Cite
@article{arxiv.2503.01724,
title = {Syntactic Learnability of Echo State Neural Language Models at Scale},
author = {Ryo Ueda and Tatsuki Kuribayashi and Shunsuke Kando and Kentaro Inui},
journal= {arXiv preprint arXiv:2503.01724},
year = {2025}
}
Comments
10 pages