Summary of the paper
| Title | Modeling and Evaluating Dialog Success in the LAST MINUTE Corpus |
|---|---|
| Authors | Dietmar Rösner, Rafael Friesen, Stephan Günther and Rico andrich |
| Abstract | The LAST MINUTE corpus comprises records and transcripts of naturalistic problem solving dialogs between N = 130 subjects and a companion system simulated in a Wizard of Oz experiment. Our goal is to detect dialog situations where subjects might break up the dialog with the system which might happen when the subject is unsuccessful. We present a dialog act based representation of the dialog courses in the problem solving phase of the experiment and propose and evaluate measures for dialog success or failure derived from this representation. This dialog act representation refines our previous coarse measure as it enables the correct classification of many dialog sequences that were ambiguous before. The dialog act representation is useful for the identification of different subject groups and the exploration of interesting dialog courses in the corpus. We find young females to be most successful in the challenging last part of the problem solving phase and young subjects to have the initiative in the dialog more often than the elderly. |
| Topics | Discourse Annotation, Representation and Processing, Dialogue |
Full paper  |
Modeling and Evaluating Dialog Success in the LAST MINUTE Corpus |
| Bibtex | @InProceedings{RSNER14.321, author = {Dietmar Rösner and Rafael Friesen and Stephan Günther and Rico andrich}, title = {Modeling and Evaluating Dialog Success in the LAST MINUTE Corpus}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |