  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.
