# SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

arXiv: https://arxiv.org/abs/2010.02322 · HTML: https://mlsanity.com/paper/2010.02322/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 30 of 52)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [C-Mixup: Improving Generalization in Regression](https://mlsanity.com/paper/2210.05775/) (2022)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency](https://mlsanity.com/paper/2203.07559/) (2022)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion](https://mlsanity.com/paper/2503.16212/) (2025)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)
- [Survey: Image Mixing and Deleting for Data Augmentation](https://mlsanity.com/paper/2106.07085/) (2021)
- [Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling](https://mlsanity.com/paper/2401.14556/) (2024)
- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach](https://mlsanity.com/paper/2209.06995/) (2022)
- [Data Augmentation for Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2109.01758/) (2021)
- [PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning](https://mlsanity.com/paper/2203.09735/) (2022)
- [Neighborhood-Regularized Self-Training for Learning with Few Labels](https://mlsanity.com/paper/2301.03726/) (2023)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs](https://mlsanity.com/paper/2406.02886/) (2024)
- [Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation](https://mlsanity.com/paper/2108.11703/) (2021)
- [ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification](https://mlsanity.com/paper/2112.11916/) (2021)
- [Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations](https://mlsanity.com/paper/2204.10714/) (2022)
- [On the Limitations of Simulating Active Learning](https://mlsanity.com/paper/2305.13342/) (2023)
- [R-Mixup: Riemannian Mixup for Biological Networks](https://mlsanity.com/paper/2306.02532/) (2023)
- [GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction](https://mlsanity.com/paper/2208.01220/) (2022)
- [LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification](https://mlsanity.com/paper/2402.16515/) (2024)
- [Self-Training with Differentiable Teacher](https://mlsanity.com/paper/2109.07049/) (2021)
- [PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners](https://mlsanity.com/paper/2205.09229/) (2022)
- [MixupE: Understanding and Improving Mixup from Directional Derivative Perspective](https://mlsanity.com/paper/2212.13381/) (2022)
- [MixKG: Mixing for harder negative samples in knowledge graph](https://mlsanity.com/paper/2202.09606/) (2022)
- [Learning to Classify Open Intent via Soft Labeling and Manifold Mixup](https://mlsanity.com/paper/2204.07804/) (2022)

## Abstract (arXiv)

Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way of leveraging human annotations. We propose a simple but effective data augmentation method to improve the label efficiency of active sequence labeling. Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration. The key difficulty is to generate plausible sequences along with token-level labels. In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples. Furthermore, we design a discriminator during sequence mixup, which judges whether the generated sequences are plausible or not. Our experiments on Named Entity Recognition and Event Detection tasks show that SeqMix can improve the standard active sequence labeling method by 2.27%--3.75% in terms of F1 scores. The code and data for SeqMix can be found at https://github.com/rz-zhang/SeqMix