  Is the human language understander a collection of modular processes
operating with relative autonomy, or is it a single integrated process? This
ongoing debate has polarized the language processing community, with two
fundamentally different types of model posited, and with each camp concluding
that the other is wrong. One camp puts forth a model with separate processors
and distinct knowledge sources to explain one body of data, and the other
proposes a model with a single processor and a homogeneous, monolithic
knowledge source to explain the other body of data. In this paper we argue that
a hybrid approach which combines a unified processor with separate knowledge
sources provides an explanation of both bodies of data, and we demonstrate the
feasibility of this approach with the computational model called COMPERE. We
believe that this approach brings the language processing community
significantly closer to offering human-like language processing systems.
