Examining the impacts of dialogue content and system automation on affect models in a spoken tutorial dialogue system

Authors:
Joanna Drummond;Diane Litman
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Year:
2011

Citing 4
Cited 2

Investigating the portability of corpus-derived cue phrases for dialogue act classification

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system

Computer Speech and Language
Domain adaptation with unlabeled data for dialog act tagging

DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor

Speech Communication

Intrinsic and extrinsic evaluation of an automatic user disengagement detector for an uncertainty-adaptive spoken dialogue system

NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Discovering habits of effective online support group chatrooms

Proceedings of the 17th ACM international conference on Supporting group work

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many dialogue system developers use data gathered from previous versions of the dialogue system to build models which enable the system to detect and respond to users' affect. Previous work in the dialogue systems community for domain adaptation has shown that large differences between versions of dialogue systems affect performance of ported models. Thus, we wish to investigate how more minor differences, like small dialogue content changes and switching from a wizarded system to a fully automated system, influence the performance of our affect detection models. We perform a post-hoc experiment where we use various data sets to train multiple models, and compare against a test set from the most recent version of our dialogue system. Analyzing these results strongly suggests that these differences do impact these models' performance.