He, Yulan and Young, S.
Due to copyright restrictions, this file is not available for public download
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1109/ASRU.2003.1318505|
|Google Scholar:||Look up in Google Scholar|
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 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/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||29 Mar 2011 10:20|
|Last Modified:||04 Oct 2016 20:26|
|Share this page:|