Automatic Detection of Miscommunication in Spoken Dialogue Systems
2015 (English)In: Proceedings of 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015, 354-363 p.Conference paper (Refereed)
In this paper, we present a data-driven approach for detecting instances of miscommunication in dialogue system interactions. A range of generic features that are both automatically extractable and manually annotated were used to train two models for online detection and one for offline analysis. Online detection could be used to raise the error awareness of the system, whereas offline detection could be used by a system designer to identify potential flaws in the dialogue design. In experimental evaluations on system logs from three different dialogue systems that vary in their dialogue strategy, the proposed models performed substantially better than the majority class baseline models.
Place, publisher, year, edition, pages
2015. 354-363 p.
Computer Science Language Technology (Computational Linguistics)
IdentifiersURN: urn:nbn:se:kth:diva-180406OAI: oai:DiVA.org:kth-180406DiVA: diva2:893730
16th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)
QC 201601202016-01-132016-01-132016-01-20Bibliographically approved