Cascaded model adaptation for dialog act segmentation and tagging
Yükleniyor...
Dosyalar
Tarih
2010-04
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
There are many speech and language processing problems which require cascaded classification tasks. While model adaptation has been shown to be useful in isolated speech and language processing tasks, it is not clear what constitutes system adaptation for such complex systems. This paper studies the following questions: In cases where a sequence of classification tasks is employed, how important is to adapt the earlier or latter systems? Is the performance improvement obtained in the earlier stages via adaptation carried on to later stages in cases where the later stages perform adaptation using similar data and/or methods? In this study, as part of a larger scale multiparty meeting understanding system, we analyze various methods for adapting dialog act segmentation and tagging models trained on conversational telephone speech (CTS) to meeting style conversations. We investigate the effect of using adapted and unadapted models for dialog act segmentation with those of tagging, showing the effect of model adaptation for cascaded classification tasks. Our results indicate that we can achieve significantly better dialog act segmentation and tagging by adapting the out-of-domain models, especially when the amount of in-domain data is limited. Experimental results show that it is more effective to adapt the models in the latter classification tasks, in our case dialog act tagging, when dealing with a sequence of cascaded classification tasks
Açıklama
This material is based upon work supported by Defense Advanced Research Projects Agency (DARPA) CALO (Contract No. FA8750-07-D-0185, Delivery Order 0004), the Scientific and Technological Research Council of Turkey (TUBITAK) fundings at SRI, Isik University Research Fund (Contract No. 0513304), J. William Fulbright Post-Doctoral Research Fellowship, and the Swiss National Science Foundation through the research network, IM2 fundings at ICS1. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. We thank Elizabeth Shriberg, Andreas Stoleke, Matthias Zimmerman, and Matthew Magimai Doss for many helpful discussions
Anahtar Kelimeler
Model adaptation, Dialog act segmentation, Dialog act tagging, Meetings processing, Speech, Recognition, System
Kaynak
Computer Speech and Language
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
24
Sayı
2
Künye
Güz, Ü., Tür, G., Hakkani Tür, D. & Cuendet, S. (2010). Cascaded model adaptation for dialog act segmentation and tagging. Computer Speech and Language, 24(2), 289-306. doi:10.1016/j.csl.2009.04.006