Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates
By Joo-Kyung Kim, Young-Bum Kim. 2018
53 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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Citations in the corpus
- Deep Unknown Intent Detection with Margin Loss (2019, background): “For example, Kim and Kim (2018) try to optimize the intent classifier and out-of-domain detector jointly, but out-of-domain samples are still needed.”