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A data-driven spoken language understanding system

He, Yulan and Young, S. (2003). A data-driven spoken language understanding system. In: 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), pp. 583–588.

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The paper presents a purely data-driven spoken language understanding (SLU) system. It consists of three major components, a speech recognizer, a semantic parser, and a dialog act decoder. A novel feature of the system is that the understanding components are trained directly from data without using explicit semantic grammar rules or fully annotated corpus data. Despite this, the system is nevertheless able to capture hierarchical structure in user utterances and handle long range dependencies. Experiments have been conducted on the ATIS corpus and 16.1% and 12.6% utterance understanding error rates were obtained for spoken input using the ATIS-3 1993 and 1994 test sets. These results show that our system is comparable to existing SLU systems which rely on either handcrafted semantic grammar rules or statistical models trained on fully-annotated training corpora, but it has greatly reduced build cost.

Item Type: Conference or Workshop Item
Copyright Holders: 2003 IEEE
Extra Information: INSPEC Accession Number: 8072860
Keywords: data-driven spoken language understanding system; dialog act decoder; fully-annotated training corpora; handcrafted semantic grammar rules; hierarchical structure; semantic parser; speech recognition; spoken dialogue systems; statistical models; utterance understanding error rates
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 23777
Depositing User: Kay Dave
Date Deposited: 29 Mar 2011 10:20
Last Modified: 16 Jun 2020 15:12
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