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The Use of Deep Learning in Spoken Dialogue Systems

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Spoken dialogue systems (SDS) provide the core enabling technology for building intelligent personal assistants. The function of an SDS is to understand each user input, decode the users intention or goal and then respond accordingly. Whereas historically much of this functionality has been provided by hand-crafted rule systems, modern systems increasingly rely on statistical models and machine learning. This talk will review the structure and components of a modern SDS and their implementation using deep neural networks. The provision of adequate quantities of annotated training data is a major limitation on progress and the talk will conclude by discussing ways in which this bottleneck might be eliminated.

This talk is part of the Cambridge Language Sciences Annual Symposium series.

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