Generalization Performance in PARSEC - A Structured Connectionist Parsing Architecture

This paper presents PARSECa system for generating connectionist parsing networks from example parses. PARSEC is not based on formal grammar systems and is geared toward spoken language tasks. PARSEC networks exhibit three strengths important for application to speech processing: 1)they learn to parse, and generalize well compared to handcoded grammars; 2) they tolerate several types of noise; 3) they can learn to use multi-modal input. Presented are the PARSEC architecture and performance analyses along several dimensions that demonstrate PARSEC's features. PARSEC's performance is compared to that of traditional grammar-basedparsing systems. 1 INTRODUCTION While a great deal of research has been done developing parsers for natural language, adequate solutionsfor some of the particular problems involved in spoken language have not been found. Among the unsolved problems are the difficulty in constructing task-specific grammars, lack of tolerance to noisy input, and inability to effectively utilize non-symbolic information.This paper describes PARSECa system for generating connectionist parsing networks from example parses.