| Challenge: | Existing co-training methods ignore sampling bias and fail to explore the data space. |
| Approach: | They propose a method to select high-quality unlabeled samples to better co-train on by learning a selection policy with a small labeled dataset. |
| Outcome: | The proposed method can obtain more accurate text classification results on clickbait detection and generic text classification tasks. |
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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
| Challenge: | Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks. |
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| Outcome: | The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset. |
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. |
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LLM-Guided Co-Training for Text Classification (2025.emnlp-main)
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| Challenge: | Empirical results show that it achieves state-of-the-art performance on 4 out of 5 benchmark datasets and ranks first among 14 compared methods according to the Friedman test. |
| Approach: | They propose a weighted co-training approach that is guided by Large Language Models (LLMs) they use LLM labels on unlabeled data as target labels and co-train two encoder-only based networks that train each other over multiple iterations. |
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A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
| Outcome: | The proposed model outperforms baseline models and reduces sensitivity to label order. |
CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) tasks require large labeled datasets to perform well. |
| Approach: | They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them. |
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Reinforced Training Data Selection for Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to learn domains with massive data are not easy to implement and require a predefined threshold. |
| Approach: | They propose a framework that searches for training instances relevant to the target domain and learns better representations for them. |
| Outcome: | The proposed framework is effective in data selection and representation, but generalized to accommodate different NLP tasks. |
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. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data (2022.findings-emnlp)
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| Challenge: | Maximum likelihood estimation (MLE) is the predominant method for training text generation models. |
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Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification (D19-1)
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| Challenge: | Existing models for multi-label classification ignore complexity and dependencies among labels . Experimental results show that our method can obtain more accurate multi-lab classification results. |
| Approach: | They propose a meta-learning method to capture complex label dependencies . they use a Meta-learner to jointly learn the training policies and prediction policies for different labels. |
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Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)
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| Challenge: | Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification. |
| Approach: | They propose a method that uses pretrained word embeddings to predict text classification . they use unlabeled data to build a classifier, and use early-stopping to improve performance . |
| Outcome: | The proposed method outperforms self-training and co-training frameworks on unlabeled data. |