Using Direct Variant Transduction for Rapid Development of Natural Spoken Interfaces
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Current speech-enabled services tend to fall into two categories: highly tuned systems requiring a large effort by specialized developers, or constrained systems that are developed rapidly but do not allow users to speak naturally. In this paper we present a new approach to language understanding aimed at bridging the gap between these extremes. This approach (direct variant transduction) relies on specifying an application with examples and on classification and pattern-matching techniques. It addresses two bottlenecks in the development of an interface with natural spoken language: coping with language variation and linking natural language to appropriate actions in the application back-end. Dialog control can be specified declaratively or be delegated to arbitrary functions computed by the underlying application. We describe the method and provide experimental results on varying the number of examples used to build a particular application.
KeywordsSpoken language understanding language variation string matching text classification dialog control
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