  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.
