  Out-of-domain (OOD) detection for low-resource text classification is a
realistic but understudied task. The goal is to detect the OOD cases with
limited in-domain (ID) training data, since we observe that training data is
often insufficient in machine learning applications. In this work, we propose
an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection
and few-shot ID classification task. Evaluation on real-world datasets show
that the proposed solution outperforms state-of-the-art methods in zero-shot
OOD detection task, while maintaining a competitive performance on ID
classification task.
