Model adaptation for dialog act tagging

Gokhan Tur, Umit Guz, Dilek Hakkani-Tür

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

Abstract

In this paper, we analyze the effect of model adaptation for dialog act tagging. The goal of adaptation is to improve the performance of the tagger using out-of-domain data or models. Dialog act tagging aims to provide a basis for further discourse analysis and understanding in conversational speech. In this study we used the ICSI meeting corpus with high-level meeting recognition dialog act (MRDA) tags, that is, question, statement, backchannel, disruptions, and floor grabbers/holders. We performed controlled adaptation experiments using the Switchboard (SWBD) corpus with SWBD-DAMSL tags as the out-of-domain corpus. Our results indicate that we can achieve significantly better dialog act tagging by automatically selecting a subset of the Switchboard corpus and combining the confidences obtained by both in-domain and out-of-domain models via logistic regression, especially when the in-domain data is limited.

Original languageEnglish (US)
Title of host publication2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006, Proceedings
Pages94-97
Number of pages4
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006 - Palm Beach, Aruba
Duration: Dec 10 2006Dec 13 2006

Publication series

Name2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006, Proceedings

Conference

Conference2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006
Country/TerritoryAruba
CityPalm Beach
Period12/10/0612/13/06

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Linguistics and Language

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