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 in [Chalmers, 1990]; however, since the pure tranformational approach does not consider the eventual inference tasks during the process of learning its representations, there is a drawback that the holistic transformation corresponding to a given inference task could become arbitrarily complex, and thus very difficult to learn. Confluent inference addresses this drawback by 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."