Novel Intent Detection and Active Learning Based Classification (Student Abstract). (arXiv:2304.11058v1 [cs.CL])

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 …

abstract active learning agents annotation arxiv classification continuous conversational conversational agents cost detection english framework human images intent detection languages novel paper research semi-supervised systems world