Learning, extracting, inserting and verifying grammatical information in recurrent neural networks

Recurrent neural networks can be trained from string examples to behave like deterministic finite-state automata (DFA's) and pushdown automata (adapts) i.e. they recognize respectively deterministic regular and context-free grammars (DCFG's). The author discusses some of the successes and failures of this type of `recurrent neural network' grammatical inference engine, as well as some of the issues of effectively using a priori symbolic knowledge in training dynamic networks. The author presents a method for networks with second-order weights where inserting prior knowledge into a network becomes a straight-forward mapping (or programming) of grammatical rules into weights. A more sophisticated hybrid machine was also developed, denoted as a neural network pushdown automata (NNPDA)-a recurrent net connected to a stack memory. This NNPDA learns to operate an external stack and recognize simple DCFG's from string examples. When hints about the grammars are given during training, the NNPDA is capable of learning more sophisticated DCFG's