Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance

Yang Xu, David Reitter


Abstract
We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication. We predict the success of collaborative task in English and Danish corpora of task-oriented dialogue. Two features are extracted from the frequency domain representations of the lexical entropy series of each interlocutor, power spectrum overlap (PSO) and relative phase (RP). We find that PSO is a negative predictor of task success, while RP is a positive one. An SVM with these features significantly improved on previous task success prediction models. Our findings suggest that the strategic distribution of information density between interlocutors is relevant to task success.
Anthology ID:
P17-1058
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
623–633
Language:
URL:
https://aclanthology.org/P17-1058
DOI:
10.18653/v1/P17-1058
Bibkey:
Cite (ACL):
Yang Xu and David Reitter. 2017. Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 623–633, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance (Xu & Reitter, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1058.pdf
Video:
 https://vimeo.com/234958072