Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to synthesize training labels with labeling rules ignore data imbalance issue . weak supervision paradigm is often used to reduce human efforts to produce training labels inexpensively. |
| Approach: | They propose a model-agnostic framework to alleviate the data imbalance issue in the weak supervision paradigm by combining labeling rules with a probabilistic margin score. |
| Outcome: | The proposed framework outperforms the state-of-the-art imbalanced learning and WS methods on four text classification datasets with four different imbalance ratios. |
Similar Papers
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing (2023.eacl-main)
Copied to clipboard
| Challenge: | Developing methods to improve model performance in imbalanced data settings has been an active area for decades . |
| Approach: | They propose to use sampling, data augmentation, choice of loss function, staged learning, or model design to address class imbalance in NLP. |
| Outcome: | The proposed approaches are evaluated on a variety of NLP tasks or in the computer vision community. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Robust Text Classifier on Test-Time Budgets (D19-1)
Copied to clipboard
| Challenge: | Recent advances in deep neural networks (DNNs) achieve high accuracy on many text classification tasks. |
| Approach: | They propose a generic framework for learning a robust text classification model . they use a data aggregation method to train the classifier on a large corpus of text . |
| Outcome: | The proposed framework achieves consistent speedup with little degradation in accuracy on four benchmark text classification tasks. |
Model-Agnostic Meta Learning for Class Imbalance Adaptation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to address class imbalance and data difficulty have been used to train models. |
| Approach: | They propose a framework that prioritizes challenging samples and minority classes over hard examples and their semantically similar neighbors to address class imbalance. |
| Outcome: | The proposed framework outperforms baselines on six imbalanced datasets and achieves substantial improvements for minority classes. |
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent datasets heuristically choose examples to ensure label balance . state-of-the-art models trained on QQP and WikiQA have only 2.4% average precision . |
| Approach: | They show that recent datasets heuristically choose examples to ensure label balance . they instead use active learning to retrieve uncertain points from a large pool of unlabeled utterance pairs . |
| Outcome: | The proposed model improves on QQP and WikiQA by using more informative negative examples. |
X-Class: Text Classification with Extremely Weak Supervision (2021.naacl-main)
Copied to clipboard
| Challenge: | Weak supervision is a problem in text classification, but it requires corpusspecific knowledge. |
| Approach: | They propose a framework for extremely weak supervision that can be used to train a text classifier. |
| Outcome: | The proposed framework outperforms seed-driven weakly supervised methods on 7 benchmark datasets. |
META: Metadata-Empowered Weak Supervision for Text Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for weakly supervised text classification use text data alone to generate pseudo-labels . strong label indicators exist in metadata and it has been long overlooked due to challenges . |
| Approach: | They propose a framework that leverages metadata as an additional source of weak supervision by combining text data and metadata into a text-rich network. |
| Outcome: | The proposed framework exploits metadata as an additional source of weak supervision. |
Open-world Multi-label Text Classification with Extremely Weak Supervision (2024.emnlp-main)
Copied to clipboard
| Challenge: | Similar single-label XWS settings cannot be easily adapted for multi-l label classification. |
| Approach: | They propose a novel method for open-world multi-label text classification under extremely weak supervision where the user provides a brief description without any labels or ground-truth label space. |
| Outcome: | The proposed method exhibits a remarkable increase in ground-truth label space coverage on various datasets. |
Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)
Copied to clipboard
Guilherme Fonseca, Washington Cunha, Gabriel Prenassi, Marcos André Gonçalves, Leonardo Chaves Dutra Da Rocha
| Challenge: | Existing methods to mitigate class imbalanced datasets are limited by existing methods. |
| Approach: | They propose two undersampling methods inspired by state-of-the-art Instance Selection techniques to mitigate class imbalance bias in ATC. |
| Outcome: | The proposed methods reduce classifier bias (56%) across all datasets without effectiveness loss while improving efficiency (1.6x speedup), scalability and reducing carbon emissions (up to 50%). |
Learning on Imbalanced Noisy Data via Debiased Sample Selection and LLM-Driven Annotation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to learning with noisy labels are prone to selection bias and training bias . obtaining large-scale high-quality datasets is expensive and time-consuming in practical scenarios . |
| Approach: | They propose an imbalanced learning with noisy labels task to let model learn from noisy labels . they first conduct debiased sample selection to better separate clean samples from noisy samples . then they feed selected clean samples to active annotator large language models for re-annotating noisy samples. |
| Outcome: | The proposed method is superior to existing methods on synthetic and real-world datasets. |