| Challenge: | State-of-the-art deep neural networks require large amounts of labeled training data that is expensive to obtain or not available for many tasks. |
| Approach: | They propose a weak supervision framework that leverages all available data for a given task . they leverage task-specific unlabeled data through self-training with a model that predicts pseudo-labels for instances that may not be covered by weak rules . |
| Outcome: | The proposed framework improves on state-of-the-art datasets on six benchmark tasks. |
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| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
| Approach: | They propose a label denoiser which estimates the source reliability using a conditional soft attention mechanism and reduces label noise by aggregating rule-annotated weak labels. |
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Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
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Meta Self-Refinement for Robust Learning with Weak Supervision (2023.eacl-main)
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| Challenge: | Recent methods leverage self-training to build noise-resistant models . however, the teacher trained under weak supervision may have fitted a substantial amount of noise and therefore produce incorrect pseudo-labels. |
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Named Entity Recognition through Deep Representation Learning and Weak Supervision (2021.findings-acl)
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META: Metadata-Empowered Weak Supervision for Text Classification (2020.emnlp-main)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
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Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
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Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
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Prototype-Representations for Training Data Filtering in Weakly-Supervised Information Extraction (2022.emnlp-industry)
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| Challenge: | Weak supervision and data programming are powerful tools to support information extraction models. |
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Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets (2023.findings-acl)
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| Challenge: | Existing models for text classification suffer from the semantic drift problem, which is a problem for self-training. |
| Approach: | They propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. |
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