Active Learning with Non-Uniform Costs for African Natural Language Processing

Challenge:	Annotating datasets for African languages is challenging due to the continent's vast linguistic diversity, complicating development of NLP systems.
Approach:	They propose a cost-aware active learning method that integrates BatchBALD acquisition strategy with a 0-1 Knapsack optimization objective to select informative and budget-efficient samples.
Outcome:	The proposed method outperforms BALD, BatchBALD, and stochastic sampling variants across cost scenarios on the MasakhaNEWS multilingual news classification benchmark covering 11 African languages.

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Overview

Challenge

Approach

Outcome

CAL-Log: Cost-Aware Active Learning with Logarithmic Cognitive Effort Modeling and Online Adaptation to Human Annotation Behavior (2026.acl-srw)

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Vihanga Supasan Kariyakaranage, Banuka Athuraliya

Text Classification

Challenge:	Standard uncertainty sampling assumes that annotating a 500-word document requires the same effort as a 50-word tweet, leading to suboptimal resource allocation when documents vary in length.
Approach:	They propose a cost-aware AL variant using logarithmic cost modeling where C(x) is the predicted annotation time for document x and L(x), is its token length.
Outcome:	Experiments on ten text classification benchmarks show a 3.3 speedup over BADGE and 3.9 over Entropy sampling to reach F1=0.80, with large effect sizes.

Active Learning for BERT: An Empirical Study (2020.emnlp-main)

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Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim

Language Models

Challenge:	Existing approaches to deal with data scarcity are active learning (AL) and pre-trained models are not being considered.
Approach:	They propose to use active learning techniques to cope with data scarcity in binary text classification scenarios where the annotation budget is very small and the data is often skewed.
Outcome:	The proposed methods improve BERT performance in binary text classification scenarios where the annotation budget is very small and the data is often skewed.

Low-resource Interactive Active Labeling for Fine-tuning Language Models (2022.findings-emnlp)

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Seiji Maekawa, Dan Zhang, Hannah Kim, Sajjadur Rahman, Estevam Hruschka

Language Models

Challenge:	Existing active learning methods for fine-tuning language models are underperforming in low-resource, interactive labeling setting.
Approach:	They propose a novel active learning method that employs a hybrid sampling strategy to minimize labeling cost and acquisition latency while providing a framework for adapting to dataset diversity.
Outcome:	The proposed method reduces labeling cost and acquisition latency while providing a framework for adapting to dataset diversity via user guidance.

Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification (2023.findings-acl)

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Lukas Wertz, Jasmina Bogojeska, Katsiaryna Mirylenka, Jonas Kuhn

Low-Resource NLP

Challenge:	Modern text classification systems achieve excellent accuracy across tasks and corpora.
Approach:	They propose a Reinforcement Learning policy that uses many different aspects of the data and task to select the most informative unlabeled subset dynamically over the course of the AL procedure.
Outcome:	The proposed framework outperforms baselines on four complex multi-class, multi-label text classification datasets.

Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages (2025.acl-srw)

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Bonaventure F. P. Dossou, Ines Arous, Jackie CK Cheung

Multilinguality

Challenge:	Existing approaches to improve model performance are finetuning on all acquired data after each round, which is computationally expensive in multilingual and low-resource settings.
Approach:	They evaluate continual finetuning (CF) against full finetuned (FA) across 28 African languages using MasakhaNEWS and SIB-200.
Outcome:	The proposed approach outperforms full finetuning (FA) in 28 African languages, achieving up to 35% reductions in GPU memory, FLOPs, and training time.

Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)

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Julia Romberg, Christopher Schröder, Julius Gonsior, Katrin Tomanek, Fredrik Olsson

Responsible & Trustworthy NLP

Challenge:	a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant .
Approach:	They conduct an online survey to assess the perceived relevance of data annotation and active learning . they propose a strategy to reduce annotation costs using active learning, an iterative process .
Outcome:	The proposed strategies reduce setup complexity and uncertainty cost while maintaining model performance.

Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)

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Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, Ambedkar Dukkipati

Machine Translation

Challenge:	Existing approaches to deep learning for NLP require large amounts of labeled data.
Approach:	They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model.
Outcome:	The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training (2023.findings-emnlp)

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Zhisong Zhang, Emma Strubell, Eduard Hovy

Responsible & Trustworthy NLP

Challenge:	Structured prediction is a fundamental problem in NLP, wherein the label space consists of complex structured outputs with groups of interdependent variables.
Approach:	They propose a partial annotation approach that selects only the most informative sub-structures for annotation and a method that incorporates the current model's automatic predictions as pseudo-labels for un-annotated sub-structurals.
Outcome:	The proposed approach reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.

Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian (2020.lrec-1)

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Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, Reza Fahmi, Omid Momenzadeh

Responsible & Trustworthy NLP

Challenge:	Existing deep learning approaches require huge amounts of data to be trained properly.
Approach:	They propose to use Persian as a model to choose the samples for annotation instead of labeling the whole dataset.
Outcome:	The proposed models achieve the baseline performance with a significantly lower amount of labeled data.

Active Learning for Abstractive Text Summarization (2022.findings-emnlp)

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Akim Tsvigun, Ivan Lysenko, Danila Sedashov, Ivan Lazichny, Eldar Damirov, Vladimir Karlov, Artemy Belousov, Leonid Sanochkin, Maxim Panov, Alexander Panchenko, Mikhail Burtsev, Artem Shelmanov

Information Extraction & Text Mining

Challenge:	Abstractive text summarization (ATS) requires a long document and short summaries.
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