Ye-Yi Wang and Alex Acero
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.
In IEEE Workshop on Automatic Speech Recognition and Understanding
Publisher Institute of Electrical and Electronics Engineers, Inc.
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