Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based
  on Word Embeddings   For most intelligent assistant systems, it is essential to have a mechanism
that detects out-of-domain (OOD) utterances automatically to handle noisy input
properly. One typical approach would be introducing a separate class that
contains OOD utterance examples combined with in-domain text samples into the
classifier. However, since OOD utterances are usually unseen to the training
datasets, the detection performance largely depends on the quality of the
attached OOD text data with restricted sizes of samples due to computing
limits. In this paper, we study how augmented OOD data based on sampling impact
OOD utterance detection with a small sample size. We hypothesize that OOD
utterance samples chosen randomly can increase the coverage of unknown OOD
utterance space and enhance detection accuracy if they are more dispersed.
Experiments show that given the same dataset with the same OOD sample size, the
OOD utterance detection performance improves when OOD samples are more
spread-out.

23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas