AL

Resisting the Lure of the Skyline: Grounding Practices in Active Learning for Morphological Inflection (2024.acl-short)

Challenge:	Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget.
Approach:	They propose to use active learning to optimize predictive performance . they find that current approaches do not generalize reliably across models and tasks .
Outcome:	The proposed approach outperforms training on i.i.d. datasets on supervised learning tasks.

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Saliha Muradoglu, Michael Ginn, Miikka Silfverberg, Mans Hulden

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

Challenge:	Several approaches to active learning are available, including confidence-based, diversity-based and committee-based.
Approach:	They propose to use a baseline and a skyline to measure the accuracy of the unannotated sample pool.
Outcome:	The proposed model outperforms a random selection baseline and a skyline approach.

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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.

ALTER: Augmentation for Large-Table-Based Reasoning (2025.naacl-long)

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Han Zhang, Yuheng Ma, Hanfang Yang

Multimodality

Challenge:	Recent studies have focused on the use of large language models (LLMs) for table-based reasoning, but most approaches struggle with scalability when applied to large tables.
Approach:	They propose a framework to harness latent augmentation potential in tabular data . they use only a small subset of relevant data from the table to supplement it with schema .
Outcome:	The proposed framework outperforms all other approaches and exhibits robustness and efficiency against perturbations in large-table scenarios.

Empirical Evaluation of Active Learning Techniques for Neural MT (D19-61)

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Xiangkai Zeng, Sarthak Garg, Rajen Chatterjee, Udhyakumar Nallasamy, Matthias Paulik

Text Generation

Challenge:	Several active learning (AL) algorithms for machine translation (MT) have been well-studied for phrase-based MT.
Approach:	They propose to use a phrase-based algorithm to compare different AL methods in a simulated AL framework to demonstrate how unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods.
Outcome:	The proposed method outperforms existing methods in the context of phrase-based MT and is based on a simulated phrase-driven dataset.

Active Learning for New Domains in Natural Language Understanding (N19-2)

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Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha, Imre Kiss, Spyros Matsoukas

FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction (2022.naacl-demo)

Challenge:	Existing approaches to improve the accuracy of new domains are lacking annotated live utterances.
Approach:	They propose an algorithm called Majority-CRF that uses an ensemble of classification models to guide the selection of relevant utterances and a sequence labeling model to prioritize informative examples.
Outcome:	The proposed algorithm achieves 6.6%-9% error rate reduction and statistically significant improvements on six new domains.

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Minh Van Nguyen, Nghia Ngo, Bonan Min, Thien Nguyen

Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores (2022.naacl-industry)

Challenge:	Existing active learning frameworks require long time between annotation batches due to time-consuming nature of model training and data selection.
Approach:	They propose a small proxy network to synchronize the proxy network with the main large model to ensure appropriateness of the selected annotation examples for the main model.
Outcome:	The proposed framework can support multiple languages and is available on github and demo website.

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Ashutosh Joshi, Shankar Vishwanath, Choon Teo, Vaclav Petricek, Vishy Vishwanathan, Rahul Bhagat, Jonathan May

Information Retrieval

Challenge:	Existing methods to augment training data for e-commerce stores using behavioral data are limited in low-traffic stores . eXtreme multi-label classification systems require large amounts of customer behavior data .
Approach:	They propose a technique that augments behavioral training data via query reformulation . they use an example semantic matching model from the e-commerce store AL-XMC .
Outcome:	The proposed method improves quality of the AL-XMC model over a baseline model.

ALANNO: An Active Learning Annotation System for Mortals (2023.eacl-demo)

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Josip Jukić, Fran Jelenić, Miroslav Bićanić, Jan Snajder

MVP-BERT: Multi-Vocab Pre-training for Chinese BERT (2021.acl-srw)

Challenge:	Active learning (AL) is a special family of machine learning algorithms designed to reduce labeling costs and improve accuracy.
Approach:	They developed an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects.
Outcome:	ALANNO is an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects.

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Wei Zhu

Cartography Active Learning (2021.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) improve performance of Chinese NLP tasks, but the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (CITATION), which is based on Chinese characters (chars).
Approach:	They propose two versions of multi-vocab pre-training (MVP) to improve Chinese PLMs' expressiveness.
Outcome:	The proposed model outperforms the recent AMBERT (CITATION) model on span-level tasks and is more robust against adversarial attacks.

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Mike Zhang, Barbara Plank

MEAL: Stable and Active Learning for Few-Shot Prompting (2023.findings-emnlp)

Challenge:	Existing methods to label data are limited in their notion of informativeness, due to post-training model uncertainty and batch diversity.
Approach:	They propose a new Active Learning algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling.
Outcome:	The proposed method is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL.

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Abdullatif Köksal, Timo Schick, Hinrich Schuetze

ALToolbox: A Set of Tools for Active Learning Annotation of Natural Language Texts (2022.emnlp-demos)

Challenge:	Existing methods for few-shot classification have high variance across different sets of few shots and finetuning runs.
Approach:	They propose novel ensembling methods that significantly reduce run variability and introduce a new active learning criterion for data selection.
Outcome:	The proposed method significantly reduces run variability and improves performance on five tasks.

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Akim Tsvigun, Leonid Sanochkin, Daniil Larionov, Gleb Kuzmin, Artem Vazhentsev, Ivan Lazichny, Nikita Khromov, Danil Kireev, Aleksandr Rubashevskii, Olga Shahmatova, Dmitry V. Dylov, Igor Galitskiy, Artem Shelmanov

Active Learning for Multilingual Semantic Parser (2023.findings-eacl)

Challenge:	Currently, the framework supports text classification, sequence tagging, and seq2seq tasks.
Approach:	They propose an open-source framework for active learning annotation in natural language processing that provides an easy-to-deploy GUI annotation tool directly in the Jupyter IDE.
Outcome:	The proposed framework reduces computational overhead and duration of AL iterations and increases annotated data reusability.

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Zhuang Li, Gholamreza Haffari

Multilinguality

Challenge:	Existing multilingual semantic parsing datasets are limited in translation effort due to data imbalance.
Approach:	They propose a first active learning procedure for multilingual semantic parsing (AL-MSP) it selects only a subset from existing datasets to be translated, they propose .
Outcome:	The proposed method significantly reduces translation costs with ideal selection methods.

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.

Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning (2025.findings-acl)

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Simret A Gebreegziabher, Kuangshi Ai, Zheng Zhang, Elena Glassman, Toby Jia-Jun Li

A Practical Incremental Learning Framework For Sparse Entity Extraction (C18-1)

Challenge:	Active Learning (AL) allows users to provide focused annotations to integrate human preferences and domain knowledge into machine learning models.
Approach:	They propose a counterfactual data augmentation approach inspired by Variation Theory to generate targeted variations along key conceptual dimensions.
Outcome:	The proposed approach achieves significantly higher performance when there are fewer annotated data, showing it can address the cold start problem in Active Learning.

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Hussein Al-Olimat, Steven Gustafson, Jason Mackay, Krishnaprasad Thirunarayan, Amit Sheth

ATGen: A Framework for Active Text Generation (2025.acl-demo)

Challenge:	Existing approaches to extract entities from textual data are expensive and unattractive due to the high cost of training.
Approach:	They propose a framework that integrates Entity Set Expansion and Active Learning to reduce the cost of data annotation.
Outcome:	The proposed framework reduces the cost of sparse entity annotation by 85% and 45% while maintaining high accuracy.

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Akim Tsvigun, Daniil Vasilev, Ivan Tsvigun, Ivan Lysenko, Talgat Bektleuov, Aleksandr Medvedev, Uliana Vinogradova, Nikita Severin, Mikhail Mozikov, Andrey Savchenko, Ilya Makarov, Grigorev Rostislav, Ramil Kuleev, Fedor Zhdanov, Artem Shelmanov

Text Generation

Challenge:	Despite the surging popularity of natural language generation tasks, the application of active learning (AL) to NLG has been limited.
Approach:	They propose a framework that bridges AL with text generation tasks and provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks.
Outcome:	The proposed framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs).

Multi-task Active Learning for Pre-trained Transformer-based Models (2022.tacl-1)

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Guy Rotman, Roi Reichart

LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks (2026.acl-srw)

Challenge:	Multi-task learning requires annotating the same text with multiple annotation schemes, which can be costly and laborious.
Approach:	They propose to use multi-task active learning paradigm to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model.
Outcome:	The proposed model minimizes annotation efforts for multi-task NLP models by iterating on the most valuable examples.

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Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang

Towards Computationally Feasible Deep Active Learning (2022.findings-naacl)

Challenge:	Deploying machine learning models in domain-specific scenarios is challenged by data drift and the scarcity of expert annotations.
Approach:	They propose a system that combines an LLM, an AL-assisted compact model and an automatic switch module to assist the active learning process.
Outcome:	The proposed system achieves 96–98% switch accuracy and outperforms both models used alone.

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Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov

On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)

Challenge:	Active learning (AL) is a technique for reducing the amount of annotation required for training machine learning models.
Approach:	They propose two techniques that reduce the amount of time required for AL . they use pseudo-labeling and distilled models to train a successor model .
Outcome:	The proposed algorithm reduces the time and computational overhead required to train an acquisition model and estimate uncertainty on instances in the unlabeled pool.

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Katerina Margatina, Loic Barrault, Nikolaos Aletras

On Dataset Transferability in Active Learning for Transformers (2023.findings-acl)

Challenge:	Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue .
Approach:	They propose to first adapt the pretrained LM to the target task and then use it for AL.
Outcome:	The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach.

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Fran Jelenić, Josip Jukić, Nina Drobac, Jan Snajder

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

Challenge:	Active learning (AL) aims to reduce labeling costs by querying the examples most beneficial for model learning.
Approach:	They propose to query examples most beneficial for model learning by querying data points most informative for labeling.
Outcome:	The proposed method reduces labeling costs by querying the examples most beneficial for model learning.

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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.

Adaptive Learning of Local Semantic and Global Structure Representations for Text Classification (C18-1)

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Jianyu Zhao, Zhiqiang Zhan, Qichuan Yang, Yang Zhang, Changjian Hu, Zhensheng Li, Liuxin Zhang, Zhiqiang He

Information Retrieval

Challenge:	Existing representation models for text classification learn little structure information or rely on pre-defined structures.
Approach:	They propose a sandwich neural network to learn local semantic and global structure representations without relying on parsers.
Outcome:	The proposed approach achieves competitive performance on several text classification tasks.

Learning How to Actively Learn: A Deep Imitation Learning Approach (P18-1)

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Ming Liu, Wray Buntine, Gholamreza Haffari

To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation (2025.coling-main)

Challenge:	Experimental results show that heuristic-based active learning methods are limited when the data distribution of the underlying learning problems vary.
Approach:	They propose a method that learns an AL "policy" using "imitation learning" they use an efficient "algorithmic expert" which provides the policy learner with good actions in the encountered AL situations.
Outcome:	The proposed method is more effective than previous methods on two tasks . labeled data is rare while unlabelled data is abundant .

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Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza

Text Generation

Challenge:	Active learning (AL) techniques reduce labeling costs for training neural machine translation models by selecting smaller representative subsets from unlabeled data for annotation.
Approach:	They propose an AL strategy that combines uncertainty and diversity for sentence selection.
Outcome:	The proposed method prioritizes diverse instances having high model uncertainty for annotation in early iterations.

Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)

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Tianze Shi, Adrian Benton, Igor Malioutov, Ozan İrsoy

Language Model-Driven Data Pruning Enables Efficient Active Learning (2026.findings-eacl)

Challenge:	a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk.
Approach:	They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning.
Outcome:	The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches.

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Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza

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

Challenge:	Existing data pruning methods for active learning are expensive and time-consuming.
Approach:	They propose a plug-and-play data pruning strategy that leverages language models to prune the unlabeled pool.
Outcome:	The proposed pruning strategy outperforms existing pruning methods on translation, sentiment analysis, topic classification, and summarization tasks on diverse datasets.

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

On the Limitations of Simulating Active Learning (2023.findings-acl)

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.

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Katerina Margatina, Nikolaos Aletras

ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)

Challenge:	Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation.
Approach:	They propose to simulate active learning by using an already labeled dataset as the pool of unlabeled data.
Outcome:	The proposed model-in-the-loop paradigm can be used to perform experiments with human annotations on-the fly.

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Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, Genghong Zhao

Structured Data in NLP

Challenge:	Existing approaches to combining knowledge Graphs (KGs) are incomplete but complementary to each other.
Approach:	They propose a novel Active Learning framework for neural EA that creates highly informative seed alignments to obtain more effective models with less annotation cost.
Outcome:	The proposed framework significantly improves sampling quality with good generality across different datasets, EA models and amount of bachelors.

On the Language Encoder of Contrastive Cross-modal Models (2024.findings-acl)

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Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji

Semantic Text Processing

Challenge:	Pretrained audio-language models such as AudioCLIP and AudioCLAP have shown promising results on vision-language (VL) tasks.
Approach:	They extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance.
Outcome:	The proposed model improves on visual-language (VL) and audio-language tasks when the amount of training data is large.

Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings (2023.emnlp-main)

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Josip Jukić, Jan Snajder

Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (D18-1)

Challenge:	Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques . data labeling is notoriously time-consuming and expensive, hindering the development of sizable labeled datasets .
Approach:	They propose to use active learning to reduce labeling costs by minimizing label complexity . they find PEFT adapter modules have significant potential in low-resource settings .
Outcome:	The proposed model outperforms FFT in low-resource settings and shows that it yields more stable representations of early and middle layers than FFT.

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Aditya Siddhant, Zachary C. Lipton

Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)

Challenge:	Existing studies on Active Learning (AL) for natural language processing have limited data requirements.
Approach:	They propose a Bayesian active learning approach that reduces deep learning's data dependence by comparing models and acquisition functions.
Outcome:	The proposed approach outperforms i.i.d. baselines and is more efficient than other approaches.

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Puria Radmard, Yassir Fathullah, Aldo Lipani

Active Learning Principles for In-Context Learning with Large Language Models (2023.findings-emnlp)

Challenge:	Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance.
Approach:	They propose to query subsequences within sentences and propagate their labels to other sentences.
Outcome:	The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data.

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Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu

D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias (2023.findings-acl)

Challenge:	In-context learning has significantly enhanced predictive performance in few-shot learning settings.
Approach:	They propose to use pool-based Active Learning to identify the most informative demonstrations for few-shot learning over a single iteration to identify best demonstrations.
Outcome:	The proposed model outperforms all other methods, including random sampling, in the analysis of 24 classification and multi-choice tasks.

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Sabit Hassan, Malihe Alikhani

Active Learning with Non-Uniform Costs for African Natural Language Processing (2026.findings-eacl)

Challenge:	Infusing clustering with active learning with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL’s annotation efficiency.
Approach:	They propose an algorithm that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate.
Outcome:	The proposed algorithm outperforms baseline AL approaches with pretrained transformers and traditional Support Vector Machines on eight datasets for emotion, hatespeech, dialog act, and book type detection tasks.

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Bonaventure F. P. Dossou, Ines Arous, Audrey Durand, Jackie Chi Kit Cheung

ALICE: Active Learning with Contrastive Natural Language Explanations (2020.emnlp-main)

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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Weixin Liang, James Zou, Zhou Yu

Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring (2021.findings-acl)

Challenge:	Annotating a large dataset with annotations is costly and infeasible.
Approach:	They propose an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning.
Outcome:	The proposed framework outperforms baseline models trained with 40-100% more training data on bird species classification and social relationship classification tasks.

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Yichi Zhang, Joyce Chai

XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)

Challenge:	despite recent progress, learning new tasks through language instructions remains a challenging problem.
Approach:	They propose a hierarchical task learning approach that decomposes task learning into three sub-problems and a model that addresses each sub-probability in a unified manner.
Outcome:	The proposed model achieves the state-of-the-art performance on the AL-FRED benchmark . it decomposes task learning into three sub-problems and addresses them in a unified manner .

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Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li, Fang Guo, Qinglin Qi, Jie Zhou, Yue Zhang

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

Challenge:	Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration.
Approach:	They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification.
Outcome:	The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions.

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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

Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)

Challenge:	Abstractive text summarization (ATS) requires a long document and short summaries.
Approach:	They propose a query strategy for AL in abstractive text summarization that uses uncertainty estimation to reduce model performance.
Outcome:	The proposed query strategy improves ROUGE and consistency scores for annotated datasets . it also increases the performance of the model, compared to passive annotation.

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Parsa Farinneya, Mohammad Mahdi Abdollah Pour, Sardar Hamidian, Mona Diab

Learning How to Active Learn by Dreaming (P19-1)

Challenge:	Existing methods for rumor tracking depend on a significant amount of labeled data.
Approach:	They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data.
Outcome:	The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model).

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Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari

Cross-Lingual Transfer

Challenge:	Recent active learning methods are limited when the data distribution of learning problems vary.
Approach:	They propose a wake-and-dream-based active learning method that learns the AL policy directly on the target domain of interest by using wake and dream cycles.
Outcome:	The proposed method improves on cross-domain and cross-lingual tasks.

A Survey of Active Learning for Natural Language Processing (2022.emnlp-main)

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Zhisong Zhang, Emma Strubell, Eduard Hovy

A Multitask Active Learning Framework for Natural Language Understanding (2020.coling-main)

Challenge:	Existing literature surveys on active learning for NLP are too specific or too general, covering deep active learning.
Approach:	They propose to use active learning to improve model learning and annotation cost for NLP problems.
Outcome:	The proposed approach is based on a large dataset of data-driven machine learning models.

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Hua Zhu, Wu Ye, Sihan Luo, Xidong Zhang

What data should I include in my POS tagging training set? (2025.findings-emnlp)

Challenge:	Existing studies have shown that relation information between intents and slots can improve the efficiency of active learning algorithms.
Approach:	They propose a multitask active learning framework that exploits relation information between sub-tasks provided by a joint model.
Outcome:	The proposed framework achieves competitive performance with less training data than baseline methods on all datasets.

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Zoey Liu, Masoud Jasbi, Christan Grant, Kenji Sagae, Emily Prud’hommeaux

Syntactic Text Processing

Challenge:	POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages .
Approach:	They compare POS tagging with in-context learning, active learning, and random sampling . they find that POS can deliver reasonable results for communities with limited resources .
Outcome:	The proposed training set for Indigenous and endangered languages performs better than random sampling.

Multilingual Detection of Personal Employment Status on Twitter (2022.acl-long)

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Manuel Tonneau, Dhaval Adjodah, Joao Palotti, Nir Grinberg, Samuel Fraiberger

Eeny, meeny, miny, moe. How to choose data for morphological inflection. (2022.emnlp-main)

Challenge:	Detecting disclosures of individuals’ employment status on social media is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them.
Approach:	They propose to use BERT-based classification models to identify five types of disclosures about individuals’ employment status in three languages.
Outcome:	The proposed methods achieve significant gains in precision, recall, and diversity of results in real-world settings of extreme class imbalance.

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Saliha Muradoglu, Mans Hulden

Morphology

Challenge:	Data scarcity is a major bottleneck for many natural language processing tasks . active learning aims to reduce the cost of data annotation by selecting the most informative examples to label.
Approach:	They propose to use oracle experiments to select data that is most informative for the model.
Outcome:	The proposed sampling strategies show that they improve on the oracle experiment and the 10-cycle iteration using Natügu as a case study.

Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis (2022.lrec-1)

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Marcello Gecchele, Hiroaki Yamada, Takenobu Tokunaga, Yasuyo Sawaki, Mika Ishizuka

Reasoning

Challenge:	In second language learning, summaries are among the most popular type of student assignments.
Approach:	They propose to revise the annotation guidelines to allow machine implementation of the new annotation guidelines.
Outcome:	The proposed algorithm achieves 0.789 precision and 0.844 recall over the L2WS 2021 corpus.

Active Learning for Natural Language Generation (2023.emnlp-main)

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Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor

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

Challenge:	Existing approaches to NLG are limited by the lack of annotated data.
Approach:	They propose to use active learning to reduce the cost of manual annotation to improve annotation efficiency by selecting the most informative examples to label.
Outcome:	The proposed approach surpasses baseline of random example selection in some cases but not in others.

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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

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

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.

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Lukas Wertz, Jasmina Bogojeska, Katsiaryna Mirylenka, Jonas Kuhn

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

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.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang

Efficient Active Learning with Adapters (2024.findings-emnlp)

Challenge:	Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point.
Approach:	They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully.
Outcome:	The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations.

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Daria Galimzianova, Leonid Sanochkin

Annotator-Centric Active Learning for Subjective NLP Tasks (2024.emnlp-main)

Challenge:	Existing studies show that distilled versions of pretrained models are not always available.
Approach:	They propose to use distilled versions of successor models as acquisition models to reduce the training cost of the model.
Outcome:	The proposed approach reduces the training cost of the model and does not cause the acquisition-successor mismatch (ASM) problem.

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Michiel Van Der Meer, Neele Falk, Pradeep Murukannaiah, Enrico Liscio

CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (2024.emnlp-main)

Challenge:	Annotator-centric active learning addresses the high costs of collecting human annotations by strategically annotating the most informative samples.
Approach:	They propose annotator-centric active learning which incorporates an annotation strategy following data sampling to approximate the full diversity of human judgments.
Outcome:	The proposed approach improves data efficiency and performs well in annotator-centric evaluations.

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Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, George Karypis

Structured Data in NLP

Challenge:	In-context learning (ICL) uses few-shot labeled examples to perform selective annotation.
Approach:	They propose an algorithm that incorporates uncertainty sampling into selective annotation for ICL . CoverICL builds a nearest-neighbor graph based on the semantic similarity between candidate ICL examples .
Outcome:	The proposed algorithm outperforms existing methods for low-budget active learning (AL) it is up to 2x more budget-efficient than SOTA methods for high-budge AL.

Rebalancing Label Distribution While Eliminating Inherent Waiting Time in Multi Label Active Learning Applied to Transformers (2024.lrec-main)

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Maxime Arens, Lucile Callebert, Mohand Boughanem, Jose G. Moreno

On the Fragility of Active Learners for Text Classification (2024.emnlp-main)

Challenge:	Data annotation is a resourceintensive endeavor, necessitating human involvement and expertise.
Approach:	They propose to annotate instances to rebalance label distribution by judiciously selecting and limiting the data to be annotated.
Outcome:	The proposed method mitigates biases, improves model performance and reduces strategy-dependent disparities.

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Abhishek Ghose, Emma Nguyen