ML p(r)ior | A Unified Process Model of Syntactic and Semantic Error Recovery in Sentence Understanding

A Unified Process Model of Syntactic and Semantic Error Recovery in Sentence Understanding

The development of models of human sentence processing has traditionally followed one of two paths. Either the model posited a sequence of processing modules, each with its own task-specific knowledge (e.g., syntax and semantics), or it posited a single processor utilizing different types of knowledge inextricably integrated into a monolithic knowledge base. Our previous work in modeling the sentence processor resulted in a model in which different processing modules used separate knowledge sources but operated in parallel to arrive at the interpretation of a sentence. One highlight of this model is that it offered an explanation of how the sentence processor might recover from an error in choosing the meaning of an ambiguous word. Recent experimental work by Laurie Stowe strongly suggests that the human sentence processor deals with syntactic error recovery using a mechanism very much like that proposed by our model of semantic error recovery. Another way to interpret Stowe's finding is this: the human sentence processor consists of a single unified processing module utilizing multiple independent knowledge sources in parallel. A sentence processor built upon this architecture should at times exhibit behavior associated with modular approaches, and at other times act like an integrated system. In this paper we explore some of these ideas via a prototype computational model of sentence processing called COMPERE, and propose a set of psychological experiments for testing our theories.

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