| 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. |
Similar Papers
Learning How to Actively Learn: A Deep Imitation Learning Approach (P18-1)
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| 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 . |
Learning a Policy for Opportunistic Active Learning (D18-1)
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| Challenge: | Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in interactive object retrieval tasks. |
| Approach: | They propose to use active learning to constrain possible queries during interactions to improve grounding of natural language descriptions in an interactive object retrieval task. |
| Outcome: | The proposed policy trades off task completion with model improvement that would benefit future tasks while lowering the cost of annotation without sacrificing model performance. |
ActiveEA: Active Learning for Neural Entity Alignment (2021.emnlp-main)
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| 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. |
Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification (2023.findings-acl)
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| 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. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
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| 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. |
Active learning for deep semantic parsing (P18-2)
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| Challenge: | Existing methods for generating training data for semantic parsing are slow and expensive. |
| Approach: | They propose active learning for "overnight" and "natural language" parsing with a logical form . they propose several active learning strategies for overnight data collection . |
| Outcome: | The proposed approach reduces the cost of training data for deep parsing tasks by reducing the number of crowd workers required. |
Practical Obstacles to Deploying Active Learning (D19-1)
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| 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. |
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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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
| 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. |
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
| 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. |
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
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| 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. |