STRUCTURED NETWORKS FOR ADAPTIVE LANGUAGE ACQUISITION

Abstract
We report on progress in understanding how to build machines which adaptively acquire the language for their task. The generic mechanism in our research has been an information-theoretic connectionist network embedded in a feedback control system. In this paper, we investigate the capability of such a network to learn associations between messages and meaningful responses to them as a task increases in size and complexity. Specifically, we consider how one might reflect task structure in a network architecture in order to provide improved generalization capability in language acquisition. We propose a method for constructing networks from component subnetworks, namely a product network, which provides improved generalization by factoring the associations between words and actions through an intermediate layer of semantic primitives. A two-dimensional product network was evaluated in a 1000-action data retrieval system, the object of which is to answer questions about 20 attributes of the 50 states of the USA. The system was tested by 13 subjects over a two-week period, during which over 1000 natural language dialogues were recorded. The experiment was conducted using typed input with unconstrained vocabulary and syntax. During the course of performing its task, the system acquired over 500 words and retained 92% of what it learned. We provide a description of the system and details on the experimental results.

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