Rule refinement with recurrent neural networks
Clyde Lee Giles, Christian Walter Peter Omlin · 2002
Recurrent neural networks can be trained to behave like deterministic finite-state automata (DFAs) and methods have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge of a subset of the DFA state transitions into recurrent neural networks, it is shown that recurrent neural networks are able to perform rule refinement. The results from training a recurrent neural network to recognize a known nontrivial randomly generated regular grammar show that not only do the networks preserve correct prior knowledge, but they are able to correct through training inserted prior knowledge which was wrong. By wrong, it is meant that the inserted rules were not the ones in the randomly generated grammar.>