# Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient

arXiv: https://arxiv.org/abs/2011.13570 · HTML: https://mlsanity.com/paper/2011.13570/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Tasks](https://mlsanity.com/paper/2306.03208/) (2023)
- [FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction](https://mlsanity.com/paper/2202.08316/) (2022)
- [Few-shot Named Entity Recognition via Superposition Concept Discrimination](https://mlsanity.com/paper/2403.16463/) (2024)
- [Actively Discovering New Slots for Task-oriented Conversation](https://mlsanity.com/paper/2305.04049/) (2023)
- [Surfing the modeling of PoS taggers in low-resource scenarios](https://mlsanity.com/paper/2402.02449/) (2024)
- [Maximally Separated Active Learning](https://mlsanity.com/paper/2411.17444/) (2024)
- [A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT](https://mlsanity.com/paper/2602.22014/) (2026)

## Abstract (arXiv)

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.