ABSTRACT
A well-known difference between human language understanding and typical computational theories of language understanding is in the degree to which they handle partial or errorful input: computational models are comparatively brittle in the face of input which deviates from the norm. In language generation there is an analogous problem, that of selecting an appropriate lexical entry when there is none in memory which matches the pragmatic/semantic input to generation. This paper presents a localized connectionist model of robust lexical selection for both language understanding and generation. Processing takes the form of pattern completion, where patterns consist of complexes of semantic, morphosyntactic, and pragmatic features. The system is presented with portions of such patterns and retrieves others. In generation the given information is pragmatic/semantic and in understanding mainly morphosyntactic. This approach is not only a natural way of accommodating both understanding and generation but it also fosters the robustness that is characteristic of human language processors.
