Abstract
Recursive productivity is considered a core property of naturallanguage and the human language faculty (Hauser, Chomsky & Fitch, 2002).It has been argued that the capacity to produce an unbounded varietyof utterances requires symbolic capabilities. Lacking structuredrepresentations, connectionist models of language processing arefrequently criticized for their failure to generalize symbolically(Hadley, 1994; Marcus, 1998).Addressing these issues, we present a neural-symbolic learning modelof sentence production, called the recursive dual-path model, whichcan cope with complex sentence structure in the form of embeddedsubordination of multiple levels.The model has separate pathways, one for mapping messages to words andone for sequence learning. The message is represented through bindingof thematic roles to concepts by weight and is inspired by spatialprocessing of visual input. In selecting syntactic frames, thesequencing system is guided by an event-semantics layer which providesinformation about clause attachment, tense, aspect, and the relativeprominence of message components.The model is tested on a structurally complex language built fromsimple clause constructions which are basic to human experience(Goldberg, 1995). We investigate the model's learning behaviorconcerning complex multi-clausal utterances and show that itsperformance matches differential trends in humans. Furthermore, weexplore its ability to produce novel embedded structures and to map`familiar' constituents to novel roles at novel sentence positions.The recursive dual-path model is joint work with Franklin Chang,postdoc researcher at NTT Communication Science Laboratory Kyoto,Japan, and based on:Chang, F. (2002) Symbolically speaking: A connectionist model ofsentence production. Cognitive Science, 26(5), 609-651.
