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.

本文提出了一种名为 NIDAL 的方法来在跨多种不同语言的情景下，实现用更少的人工注释成本来检测新意图。实验证明该方法在各种基准数据集上皆表现优异，相较于基线方法缩小了10%以上的精度误差，且能够仅仅利用系统可用的未标注数据量的6-10%来维持识别成本。

基于新颖意图检测和主动学习的分类