Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Learning of Domain Classification and Out-of-Domain Detection with
  Dynamic Class Weighting for Satisficing False Acceptance Rates   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.

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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg