Learning Recursive Distributed Representations for Holistic Computation

Abstract

A number of connectionist models capable of representing data with compositional structure have recently appeared. These new models suggest the intriguing possibility of performing holistic structure-sensitive computations with distributed representations. Two possible forms of holistic inference, transformational inference and confluent inference, are identified and compared. Transformational inference was successfully demonstrated by Chalmers; however, the pure transformational approach does not consider the eventual inference tasks during the process of learning its representations. Confluent inference is introduced as a method for achieving a tight coupling between the distributed representations of a problem and the solution for the given inference task while the net is still learning its representations. A dual-ported RAAM architecture based on Pollack's Recursive Auto-Associative Memory is implemented and demonstrated in the domain of Natural Language translation.