A structured network architecture for adaptive language acquisition

The authors report on the progress in understanding how to build devices which adaptively acquire the language for their task. The generic device is an information-theoretic connectionist network embedded in a feedback control system. They investigate the capability of the network to learn associations between messages and meaningful responses to them as a task increases in size and complexity. Specifically, the authors consider how one might reflect task structure in the network architecture in order to provide improved generalization capability in language acquisition. They propose a product network, which provides improved generalization by factoring the associations between words and action through semantic primitives. The product network is being evaluated in several experimental systems, including a 1000-action Almanac data retrieval system. They describe these systems and provide details on two preliminary experiments