  Novel intent class detection is an important problem in real world scenario
for conversational agents for continuous interaction. Several research works
have been done to detect novel intents in a mono-lingual (primarily English)
texts and images. But, current systems lack an end-to-end universal framework
to detect novel intents across various different languages with less human
annotation effort for mis-classified and system rejected samples. This paper
proposes NIDAL (Novel Intent Detection and Active Learning based
classification), a semi-supervised framework to detect novel intents while
reducing human annotation cost. Empirical results on various benchmark datasets
demonstrate that this system outperforms the baseline methods by more than 10%
margin for accuracy and macro-F1. The system achieves this while maintaining
overall annotation cost to be just ~6-10% of the unlabeled data available to
the system.
