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arXiv · 2605.31421

Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm

Fabio Massimo Zanzotto·Federico Ranaldi·Giorgio Satta
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Abstract

In this paper, we show the possibility of a direct injection of algorithms into neural network architecture. We focus on a complex algorithm, that is, Cocke-Youger-Kasami (CYK) for parsing context-free grammars in Chomsky Normal Form and we propose CYKNN, a simple recurrent neural network architecture for encoding the CYK algorithm in trainable matrix-vector multiplications.We experimented with a very simple grammar with 4 variations showing that our approach outperforms existing LLMs with more than 20B parameters with an in-context learning setting and smaller LLMs of the Qwen family fine-tuned with LoRA. Our attempt paves the way to a different approach to neuro-symbolic methodologies.

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Fabio Massimo Zanzotto, Federico Ranaldi, Giorgio Satta. 2026-05-29. Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm. https://arxiv.org/abs/2605.31421

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