Summary of the paper

Title Evaluation of HMM-based Models for the Annotation of Unsegmented Dialogue Turns
Authors Carlos-D. Martínez-Hinarejos, Vicent Tamarit and José-M. Benedí
Abstract Corpus-based dialogue systems rely on statistical models, whoseparameters are inferred from annotated dialogues. The dialogues are usuallyannotated in terms of Dialogue Acts (DA), and the manual annotation isdifficult(as annotation rule are hard to define), error-prone and time-consuming. Therefore, several semi-automatic annotation processes have been proposed to speed-up the process and consequently obtain a dialogue system in lesstotal time. These processes are usually based on statistical models. The standard statistical annotation model is based on Hidden Markov Models (HMM). In this work, we explore the impact of different types of HMM, with different number of states, on annotation accuracy. We performed experiments using these models on two dialogue corpora (Dihana and SwitchBoard) of dissimilar features.The results show that some types of models improve standard HMM in a human-computer task-oriented dialogue corpus (Dihana corpus), but their impact is lower in a human-human non-task-oriented dialogue corpus (SwitchBoardcorpus).
Language Tools, systems, applications
Topics Dialogue, Corpus (creation, annotation, etc.), Tools, systems, applications
Full paper Evaluation of HMM-based Models for the Annotation of Unsegmented Dialogue Turns
Bibtex @InProceedings{MARTNEZHINAREJOS10.303,
  author = {Carlos-D. Martínez-Hinarejos, Vicent Tamarit and José-M. Benedí},
  title = {Evaluation of HMM-based Models for the Annotation of Unsegmented Dialogue Turns},
  booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
Powered by ELDA © 2010 ELDA/ELRA