Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in a user’s utterance. Traditionally the problem is solved with a knowledge-based approach. In the past decade many data-driven statistical models have been proposed, all of them are in the generative framework. In our previous study, we have introduced a HMM/CFG composite model. It is a generative model that integrates knowledge-based approach in a statistical learning framework. We have investigated similar integration of prior knowledge and statistical learning in the framework of conditional models recently. This extended summary describes our experiences and presents some preliminary results, which shows a 17% slot error rate reduction over the generative model.

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In  NIPS Workshop: Advances in Structured Learning for Text and Speech Processing

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