Processing of nested and cross-serial dependencies: an automaton perspective on SRN behaviour
Language processing involves the identification and establishment of both nested (stack-like) and cross-serial (queue-like) dependencies. This paper analyses the behaviour of simple recurrent networks (SRNs) trained to handle these types of dependency individually and simultaneously. We provide new converging evidence that SRNs store sequences in a fractal data structure similar to a binary expansion. We provide evidence that the process of recalling a stored string by an SRN depletes the stored data structure, much like the operations of a symbolic stack or queue. Trained networks do not seem to operate like random access arrays, where a pointer into a data structure can retrieve data without altering the contents of the data structure. In addition, we demonstrate that networks trained to model both types of dependencies do not implement a more complex, but unified, representation, but rather implement two independent data structures, similar to a stack and queue.
nested and cross-serial dependencies srns fractal representations multi-task networks network implementation of symbolic structure