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Lexico-acoustic Neural-based Models for Dialog Act Classification

Abstract: 

Recent works have proposed neural models for dialog act classification in spoken dialogs.
However, they have not explored the role and the usefulness of acoustic information.
We propose a neural model that processes both lexical and acoustic features for classification.
Our results on two benchmark datasets reveal that acoustic features are helpful in improving the overall accuracy.
Finally, a deeper analysis shows that acoustic features are valuable in three cases: when a dialog act has sufficient data, when lexical information is limited and when strong lexical cues are not present.

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Paper Details

Authors:
Daniel Ortega, Ngoc Thang Vu
Submitted On:
14 April 2018 - 12:45am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Daniel Ortega
Paper Code:
4427
Document Year:
2018
Cite

Document Files

icassp-2018-poster.pdf

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[1] Daniel Ortega, Ngoc Thang Vu, "Lexico-acoustic Neural-based Models for Dialog Act Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2796. Accessed: May. 21, 2019.
@article{2796-18,
url = {http://sigport.org/2796},
author = {Daniel Ortega; Ngoc Thang Vu },
publisher = {IEEE SigPort},
title = {Lexico-acoustic Neural-based Models for Dialog Act Classification},
year = {2018} }
TY - EJOUR
T1 - Lexico-acoustic Neural-based Models for Dialog Act Classification
AU - Daniel Ortega; Ngoc Thang Vu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2796
ER -
Daniel Ortega, Ngoc Thang Vu. (2018). Lexico-acoustic Neural-based Models for Dialog Act Classification. IEEE SigPort. http://sigport.org/2796
Daniel Ortega, Ngoc Thang Vu, 2018. Lexico-acoustic Neural-based Models for Dialog Act Classification. Available at: http://sigport.org/2796.
Daniel Ortega, Ngoc Thang Vu. (2018). "Lexico-acoustic Neural-based Models for Dialog Act Classification." Web.
1. Daniel Ortega, Ngoc Thang Vu. Lexico-acoustic Neural-based Models for Dialog Act Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2796