Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm
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Computer Science > Computation and Language
Title:Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm
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 this http URL 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.
| Comments: | 9 content pages |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Data Structures and Algorithms (cs.DS) |
| Cite as: | arXiv:2605.31421 [cs.CL] |
| (or arXiv:2605.31421v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.31421
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Fabio Massimo Zanzotto [view email][v1] Fri, 29 May 2026 15:21:11 UTC (969 KB)
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