Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Sentence Embedding using Only In-domain Sentences for
  Out-of-domain Sentence Detection in Dialog Systems   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.

23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom