Title: GRAMMAR LEARNING FOR SPOKEN LANGUAGE UNDERSTANDING

Authors: Ye-Yi Wang, Alex Acero

Abstract: 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 rapidly develop 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 amount 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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