Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
| 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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| 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. |
On the Limitations of Simulating Active Learning (2023.findings-acl)
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| 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. |
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Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
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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 . |
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| Challenge: | Active learning (AL) techniques optimally utilize a labeling budget by iteratively selecting instances that are most valuable for learning. |
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| 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 . |
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Efficient Active Learning with Adapters (2024.findings-emnlp)
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| 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. |
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| Challenge: | Existing studies on Active Learning (AL) for natural language processing have limited data requirements. |
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Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
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| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
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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
| Challenge: | Existing approaches to deal with data scarcity are active learning (AL) and pre-trained models are not being considered. |
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