  In a corpus of data, outliers are either errors: mistakes in the data that
are counterproductive, or are unique: informative samples that improve model
robustness. Identifying outliers can lead to better datasets by (1) removing
noise in datasets and (2) guiding collection of additional data to fill gaps.
However, the problem of detecting both outlier types has received relatively
little attention in NLP, particularly for dialog systems. We introduce a simple
and effective technique for detecting both erroneous and unique samples in a
corpus of short texts using neural sentence embeddings combined with
distance-based outlier detection. We also present a novel data collection
pipeline built atop our detection technique to automatically and iteratively
mine unique data samples while discarding erroneous samples. Experiments show
that our outlier detection technique is effective at finding errors while our
data collection pipeline yields highly diverse corpora that in turn produce
more robust intent classification and slot-filling models.
