Grammar Learning for Spoken Language Understanding

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.

PDF file

In  IEEE Workshop on Automatic Speech Recognition and Understanding

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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Pages292- 295
AddressMadonna di Campiglio, Italy
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