Instantiating thematic roles with a recurrent neural network
Abstract
Human languages have both a surface structure and a deep structure. The surface structure
of a language can be described by grammatical rules which generate its well-formed sentences.
Deep structure can be described by a set of thematic roles which specify the meaning of a
sentence independent of any particular language. In this paper, I show that a recurrent neural
network can learn to map English sentences generated by a unification-based grammar onto their
appropriate thematic roles. Two models are evaluated: (1) a simple recurrent network like those
of Jordan (1986) and Elman (1990), and (2) a modified recurrent network which incorporates
a time-varying training signal, direct connections between its input and output units, and two
separate state layers. It is found that the modified network learns faster and more completely
than the simple recurrent network.