Filtering conversations through dialogue acts labels for improving corpus-based convergence studies

Simone Fuscone, Benoit Favre, Laurent Prévot


Abstract
Cognitive models of conversation and research on user-adaptation in dialogue systems involves a better understanding of speakers convergence in conversation. Convergence effects have been established on controlled data sets, for various acoustic and linguistic variables. Tracking interpersonal dynamics on generic corpora has provided positive but more contrasted outcomes. We propose here to enrich large conversational corpora with dialogue act (DA) information. We use DA-labels as filters in order to create data sub sets featuring homogeneous conversational activity. Those data sets allow a more precise comparison between speakers’ speech variables. Our experiences consist of comparing convergence on low level variables (Energy, Pitch, Speech Rate) measured on raw data sets, with human and automatically DA-labelled data sets. We found that such filtering does help in observing convergence suggesting that studies on interpersonal dynamics should consider such high level dialogue activity types and their related NLP topics as important ingredients of their toolboxes.
Anthology ID:
2020.sigdial-1.25
Volume:
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
July
Year:
2020
Address:
1st virtual meeting
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
203–208
Language:
URL:
https://www.aclweb.org/anthology/2020.sigdial-1.25
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.sigdial-1.25.pdf
Video:
 https://youtube.com/watch?v=ZDB3JaLVU08