# Density-Based Dynamic Curriculum Learning for Intent Detection

arXiv: https://arxiv.org/abs/2108.10674 · HTML: https://mlsanity.com/paper/2108.10674/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 24 Aug 2021.

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

- [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)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [Norm-Based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/2006.02014/) (2020)
- [Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking](https://mlsanity.com/paper/1912.08555/) (2019)

## Built on by (most cited first, 7 of 7)

- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text](https://mlsanity.com/paper/2502.12953/) (2025)
- [Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss](https://mlsanity.com/paper/2203.09278/) (2022)
- [Learning to Personalize Recommendation based on Customers' Shopping Intents](https://mlsanity.com/paper/2305.05279/) (2023)
- [CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning](https://mlsanity.com/paper/2601.05858/) (2026)
- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)
- [Dual-Criterion Curriculum Learning: Application to Temporal Data](https://mlsanity.com/paper/2603.23573/) (2026)

## Abstract (arXiv)

Pre-trained language models have achieved noticeable performance on the intent detection task. However, due to assigning an identical weight to each sample, they suffer from the overfitting of simple samples and the failure to learn complex samples well. To handle this problem, we propose a density-based dynamic curriculum learning model. Our model defines the sample's difficulty level according to their eigenvectors' density. In this way, we exploit the overall distribution of all samples' eigenvectors simultaneously. Then we apply a dynamic curriculum learning strategy, which pays distinct attention to samples of various difficulty levels and alters the proportion of samples during the training process. Through the above operation, simple samples are well-trained, and complex samples are enhanced. Experiments on three open datasets verify that the proposed density-based algorithm can distinguish simple and complex samples significantly. Besides, our model obtains obvious improvement over the strong baselines.