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Grammar Learning for Spoken Language Understanding

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.


Publication typeInproceedings
Published inIEEE Workshop on Automatic Speech Recognition and Understanding
Pages292- 295
AddressMadonna di Campiglio, Italy
PublisherInstitute of Electrical and Electronics Engineers, Inc.
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