Exploiting distance based similarity in topic models for user intent detection

Asli Celikyilmaz, Dilek Hakkani-Tur, Gokhan Tur, Ashley Fidler, Dustin Hillard

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

Abstract

One of the main components of spoken language understanding is intent detection, which allows user goals to be identified. A challenging sub-task of intent detection is the identification of intent bearing phrases from a limited amount of training data, while maintaining the ability to generalize well. We present a new probabilistic topic model for jointly identifying semantic intents and common phrases in spoken language utterances. Our model jointly learns a set of intent dependent phrases and captures semantic intent clusters as distributions over these phrases based on a distance dependent sampling method. This sampling method uses proximity of words utterances when assigning words to latent topics. We evaluate our method on labeled utterances and present several examples of discovered semantic units. We demonstrate that our model outperforms standard topic models based on bag-of-words assumption.

Original languageEnglish (US)
Title of host publication2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
Pages425-430
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 - Waikoloa, HI, United States
Duration: Dec 11 2011Dec 15 2011

Publication series

Name2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings

Conference

Conference2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011
Country/TerritoryUnited States
CityWaikoloa, HI
Period12/11/1112/15/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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