Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates

Joo-Kyung Kim, Young-Bum Kim


In domain classification for spoken dialog systems, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems. Previous work usually utilizes OOD detectors that are trained separately from in-domain (IND) classifiers and confidence thresholding for OOD detection given target evaluation scores. In this paper, we introduce a neural joint learning model for domain classification and OOD detection, where dynamic class weighting is used during the model training to satisfice a given OOD false acceptance rate (FAR) while maximizing the domain classification accuracy. Evaluating on two domain classification tasks for the utterances from a large spoken dialogue system, we show that our approach significantly improves the domain classification performance with satisficing given target FARs.


 DOI: 10.21437/Interspeech.2018-1581

Cite as: Kim, J., Kim, Y. (2018) Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates. Proc. Interspeech 2018, 556-560, DOI: 10.21437/Interspeech.2018-1581.


@inproceedings{Kim2018,
  author={Joo-Kyung Kim and Young-Bum Kim},
  title={Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={556--560},
  doi={10.21437/Interspeech.2018-1581},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1581}
}