  The ability to detect Out-of-Domain (OOD) inputs has been a critical
requirement in many real-world NLP applications. For example, intent
classification in dialogue systems. The reason is that the inclusion of
unsupported OOD inputs may lead to catastrophic failure of systems. However, it
remains an empirical question whether current methods can tackle such problems
reliably in a realistic scenario where zero OOD training data is available. In
this study, we propose ProtoInfoMax, a new architecture that extends
Prototypical Networks to simultaneously process in-domain and OOD sentences via
Mutual Information Maximization (InfoMax) objective. Experimental results show
that our proposed method can substantially improve performance up to 20% for
OOD detection in low resource settings of text classification. We also show
that ProtoInfoMax is less prone to typical overconfidence errors of Neural
Networks, leading to more reliable prediction results.
