Model adaptation for dialog act tagging
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Dosyalar
Tarih
2006
Yazarlar
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Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Adaptation model, Automatic control, Computer science, Conversational speech, Dialog act tagging, Dialogue policy, Discourse analysis, Domain modeling, Electric switchboards, Floors, Food processing, High-level meeting recognition dialog act, Humans, ICSI meeting corpus, Interactive systems, Interpolation, Linguistics, Logistic regression, Logistic regression (LR), Logistics, Materials handling, Meeting recognition, Model adaptation, Models, Natural language processing, Natural languages, Out-of-domain data, Query languages, Speech analysis, Speech processing, Speech recognition, Spoken languages, SWBD-DAMSL tags, Switchboard corpus, Tagging
Kaynak
2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006, Proceedings
WoS Q Değeri
N/A
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Sayı
Künye
Tür, G., Güz, Ü. & Hakkani Tür, D. (2006). Model adaptation for dialog act tagging. Paper presented at the 2006 IEEE ACL Spoken Language Technology Workshop, SLT 2006, Proceedings, 94-97. doi:10.1109/SLT.2006.326825
