  To ensure satisfactory user experience, dialog systems must be able to
determine whether an input sentence is in-domain (ID) or out-of-domain (OOD).
We assume that only ID sentences are available as training data because
collecting enough OOD sentences in an unbiased way is a laborious and
time-consuming job. This paper proposes a novel neural sentence embedding
method that represents sentences in a low-dimensional continuous vector space
that emphasizes aspects that distinguish ID cases from OOD cases. We first used
a large set of unlabeled text to pre-train word representations that are used
to initialize neural sentence embedding. Then we used domain-category analysis
as an auxiliary task to train neural sentence embedding for OOD sentence
detection. After the sentence representations were learned, we used them to
train an autoencoder aimed at OOD sentence detection. We evaluated our method
by experimentally comparing it to the state-of-the-art methods in an
eight-domain dialog system; our proposed method achieved the highest accuracy
in all tests.
