# SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model

arXiv: https://arxiv.org/abs/2310.07306 · HTML: https://mlsanity.com/paper/2310.07306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Adversarial Reciprocal Points Learning for Open Set Recognition](https://mlsanity.com/paper/2103.00953/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [Noisy Feature Mixup](https://mlsanity.com/paper/2110.02180/) (2021)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)

## Abstract (arXiv)

This paper presents a Soft Labeling and Noisy Mixup-based open intent classification model (SNOiC). Most of the previous works have used threshold-based methods to identify open intents, which are prone to overfitting and may produce biased predictions. Additionally, the need for more available data for an open intent class presents another limitation for these existing models. SNOiC combines Soft Labeling and Noisy Mixup strategies to reduce the biasing and generate pseudo-data for open intent class. The experimental results on four benchmark datasets show that the SNOiC model achieves a minimum and maximum performance of 68.72% and 94.71%, respectively, in identifying open intents. Moreover, compared to state-of-the-art models, the SNOiC model improves the performance of identifying open intents by 0.93% (minimum) and 12.76% (maximum). The model's efficacy is further established by analyzing various parameters used in the proposed model. An ablation study is also conducted, which involves creating three model variants to validate the effectiveness of the SNOiC model.