Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910272455704576 |
|---|---|
| author | Zanzotto, Fabio Massimo Ranaldi, Federico Satta, Giorgio |
| author_facet | Zanzotto, Fabio Massimo Ranaldi, Federico Satta, Giorgio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31421 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm Zanzotto, Fabio Massimo Ranaldi, Federico Satta, Giorgio Computation and Language Artificial Intelligence Data Structures and Algorithms 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. |
| title | Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm |
| topic | Computation and Language Artificial Intelligence Data Structures and Algorithms |
| url | https://arxiv.org/abs/2605.31421 |