Synthesizing Context-free Grammars from Recurrent Neural Networks (Extended Version)

Autor:	Yellin, Daniel M., Weiss, Gail
Rok vydání:	2021
Předmět:	Computer Science - Formal Languages and Automata Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present an algorithm for extracting a subclass of the context free grammars (CFGs) from a trained recurrent neural network (RNN). We develop a new framework, pattern rule sets (PRSs), which describe sequences of deterministic finite automata (DFAs) that approximate a non-regular language. We present an algorithm for recovering the PRS behind a sequence of such automata, and apply it to the sequences of automata extracted from trained RNNs using the L* algorithm. We then show how the PRS may converted into a CFG, enabling a familiar and useful presentation of the learned language. Extracting the learned language of an RNN is important to facilitate understanding of the RNN and to verify its correctness. Furthermore, the extracted CFG can augment the RNN in classifying correct sentences, as the RNN's predictive accuracy decreases when the recursion depth and distance between matching delimiters of its input sequences increases. Comment: Extended version of paper to appear in TACAS 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.08200 Zobrazit plný text záznamu View this record from Arxiv