PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug
  andPlay Data Augmentation
  While Out-of-distribution (OOD) detection has been well explored in computer
vision, there have been relatively few prior attempts in OOD detection for NLP
classification. In this paper we argue that these prior attempts do not fully
address the OOD problem and may suffer from data leakage and poor calibration
of the resulting models. We present PnPOOD, a data augmentation technique to
perform OOD detection via out-of-domain sample generation using the recently
proposed Plug and Play Language Model (Dathathri et al., 2020). Our method
generates high quality discriminative samples close to the class boundaries,
resulting in accurate OOD detection at test time. We demonstrate that our model
outperforms prior models on OOD sample detection, and exhibits lower
calibration error on the 20 newsgroup text and Stanford Sentiment Treebank
dataset (Lang, 1995; Socheret al., 2013). We further highlight an important
data leakage issue with datasets used in prior attempts at OOD detection, and
share results on a new dataset for OOD detection that does not suffer from the
same problem.
