Achieving Robustness through the Integration of Language Production in Comprehension

Language comprehension in humans is very robust. With apparent ease, language users can grasp the meaning of utterances made in very bad acoustic conditions or containing grammatical errors. A key mechanism in achieving this robustness is the ability of the listener to predict what the speaker is likely to say. According to recent work in psycholinguistics, these predictions are made by covertly simulating the speaker while he is speaking and thus integrating production in comprehension. This article presents a model which makes use of this idea in order to enhance the robustness of language processing in intelligent systems, in particular to recover the intended meaning of utterances containing grammatical errors. The model is implemented in Fluid Construction Grammar and applied to a case study for the complex Dutch Verb Phrase.