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Grammar learning for spoken language understanding

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2 Author(s)
Ye-Yi Wang ; Microsoft Res., Redmond, WA, USA ; Acero, A.

Many state-of-the-art conversational systems use semantic-based robust understanding and manually derived grammars, a very time-consuming and error-prone process. This paper describes a machine-aided grammar authoring system that enables a programmer to develop rapidly a high quality grammar for conversational systems. This is achieved with a combination of domain-specific semantics, a library grammar, syntactic constraints and a small number of example sentences that have been semantically annotated. Our experiments show that the learned semantic grammars consistently outperform manually authored grammars, requiring much less authoring load.

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Automatic Speech Recognition and Understanding, 2001. ASRU '01. IEEE Workshop on

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