# Pre-trained Language Model Based Active Learning for Sentence Matching

arXiv: https://arxiv.org/abs/2010.05522 · HTML: https://mlsanity.com/paper/2010.05522/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

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

- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Multi-task Active Learning for Pre-trained Transformer-based Models](https://mlsanity.com/paper/2208.05379/) (2022)
- [D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias](https://mlsanity.com/paper/2305.17013/) (2023)
- [Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios](https://mlsanity.com/paper/2410.11114/) (2024)
- [Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents](https://mlsanity.com/paper/2410.14141/) (2024)
- [Inconsistency-Based Data-Centric Active Open-Set Annotation](https://mlsanity.com/paper/2401.04923/) (2024)
- [Active Learning with Task Adaptation Pre-training for Speech Emotion Recognition](https://mlsanity.com/paper/2405.00307/) (2024)

## Abstract (arXiv)

Active learning is able to significantly reduce the annotation cost for data-driven techniques. However, previous active learning approaches for natural language processing mainly depend on the entropy-based uncertainty criterion, and ignore the characteristics of natural language. In this paper, we propose a pre-trained language model based active learning approach for sentence matching. Differing from previous active learning, it can provide linguistic criteria to measure instances and help select more efficient instances for annotation. Experiments demonstrate our approach can achieve greater accuracy with fewer labeled training instances.