Novel Intent Detection and Active Learning Based Classification (Student Abstract)
This addresses the need for more efficient and universal intent detection in conversational agents, though it appears incremental as it builds on existing methods for novel intent detection.
The paper tackles the problem of detecting novel intents across multiple languages with reduced human annotation effort, proposing NIDAL, a semi-supervised framework that outperforms baselines by over 10% in accuracy and macro-F1 while keeping annotation costs to 6-10% of unlabeled data.
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.