# Novel Intent Detection and Active Learning Based Classification (Student Abstract)

arXiv: https://arxiv.org/abs/2304.11058 · HTML: https://mlsanity.com/paper/2304.11058/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [Out-of-Domain Detection for Low-Resource Text Classification Tasks](https://mlsanity.com/paper/1909.05357/) (2019)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)

## Built on by (most cited first, 6 of 6)

- [Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection](https://mlsanity.com/paper/2508.16122/) (2025)
- [Intent Detection and Entity Extraction from BioMedical Literature](https://mlsanity.com/paper/2404.03598/) (2024)
- [On The Persona-based Summarization of Domain-Specific Documents](https://mlsanity.com/paper/2406.03986/) (2024)
- [A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents](https://mlsanity.com/paper/2410.22476/) (2024)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)
- [IDALC: A Semi-Supervised Framework for Intent Detection and Active Learning based Correction](https://mlsanity.com/paper/2511.05921/) (2025)

## Abstract (arXiv)

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.