Connectionist networks are popular for many of the same reasons as statistical techniques.
They have effective learning algorithms and are generally robust. They also have the advantage of learning their own internal representations, so they are less constrained by the way the system designer formulates the problem. However, their success has been limited to pattern matching tasks. In this talk I will discuss initial experiments on training TSVB networks to do broad coverage syntactic parsing. In the current experiments these Simple Synchrony Networks take a sequence of word tags as input and produce a sequence of statements about parent-child relationships.