Synthesizing context-free grammars from recurrent neural networks

DOI10.1007/978-3-030-72016-2_19zbMath1467.68077arXiv2101.08200OpenAlexW3135979110MaRDI QIDQ2044212

Gail Weiss, Daniel M. Yellin

Publication date: 4 August 2021

Full work available at URL: https://arxiv.org/abs/2101.08200

zbMATH Keywords

recurrent neural networksfinite-state machinesmodel extractionlearning context-free grammars

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Formal languages and automata (68Q45) Grammars and rewriting systems (68Q42)

Related Items (4)

Unsupervised and few-shot parsing from pretrained language models ⋮ Learning finite state models from recurrent neural networks ⋮ A survey of model learning techniques for recurrent neural networks ⋮ Synthesizing context-free grammars from recurrent neural networks

Cites Work

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• A Polynomial Algorithm for the Inference of Context Free Languages
• Inductive inference of formal languages from positive data
• Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review
• Language identification in the limit

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