Sentence simplification for spoken language understanding

Gokhan Tur, Dilek Hakkani-Tür, Larry Heck, S. Parthasarathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a sentence simplification method and demonstrate its use to improve intent determination and slot filling tasks in spoken language understanding (SLU) systems. This research is motivated by the observation that, while current statistical SLU models usually perform accurately for simple, well-formed sentences, error rates increase for more complex, longer, more natural or spontaneous utterances. Furthermore, users familiar with web search usually formulate their information requests as a keyword search query, suggesting that frameworks which can handle both forms of inputs is required. We propose a dependency parsing-based sentence simplification approach that extracts a set of keywords from natural language sentences and uses those in addition to entire utterances for completing SLU tasks. We evaluated this approach using the well-studied ATIS corpus with manual and automatic transcriptions and observed significant error reductions for both intent determination (30% relative) and slot filling (15% relative) tasks over the state-of-the-art performances.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5628-5631
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • dependency parsing
  • intent determination
  • semantic parsing
  • sentence simplification
  • slot filling
  • spoken language understanding

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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