| 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. |
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Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)
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| Challenge: | Existing methods to reduce model's reliance on bias features ignore the learnability of these features. |
| Approach: | They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features. |
| Outcome: | The proposed models can perform better on out-of-distribution datasets than baseline models with a more sophisticated model design. |
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification (2022.findings-emnlp)
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| 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. |
FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification (2022.emnlp-main)
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| Challenge: | Recent research shows keyword-driven methods can achieve state-of-the-art performance on various tasks. |
| Approach: | They propose an efficient weakly-supervised text classification approach using unlabeled data . they use dense text representation to retrieve class-relevant documents from unlabed corpus . |
| Outcome: | The proposed weakly-supervised classification method outperforms keyword-driven models on a wide range of classification tasks. |
On the Fragility of Active Learners for Text Classification (2024.emnlp-main)
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| Challenge: | Active learning (AL) techniques optimally utilize a labeling budget by iteratively selecting instances that are most valuable for learning. |
| Approach: | They propose to use active learning techniques to iteratively select instances that are most valuable for learning. |
| Outcome: | The proposed framework is used to benchmark active learning techniques for text classification using pre-trained representations. |
Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers (2022.findings-acl)
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| Challenge: | A series of benchmarking experiments based on three different datasets and three state-of-the-art classifiers show that our framework can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) |
| Approach: | They propose a semi-automated approach that passes unconfident, probably incorrect classifications to human moderators to minimize the workload. |
| Outcome: | The proposed approach can improve the classification F1-scores by 5.1 to 11.2% (up to approx. 98 to 99%) while reducing the moderation load up to 73.3% compared to a random moderation. |
Smaller Text Classifiers with Discriminative Cluster Embeddings (N18-2)
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| Challenge: | Word embeddings dominate overall model sizes in neural methods for natural language processing, especially when large vocabularies and high dimensions are used. |
| Approach: | They propose a Gumbel-Softmax distribution to maximize over the latent clustering while minimizing the task loss. |
| Outcome: | The proposed method minimizes the task loss while maximizing over the latent clustering while remaining parameter-efficient. |
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data (2021.emnlp-main)
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| Challenge: | Existing text classification methods focus on a fixed label set, but many real-world applications require extending to new fine-grained classes as the number of samples per label increases. |
| Approach: | They propose a problem called coarse-to-fine grained classification that leverages label surface names as the only human guidance. |
| Outcome: | The proposed method outperforms existing methods on two real-world datasets. |
Instance-Selection-Inspired Undersampling Strategies for Bias Reduction in Small and Large Language Models for Binary Text Classification (2025.acl-long)
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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%). |
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting (2022.findings-acl)
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| Challenge: | Large-scale multi-label document classification presents interesting challenges due to the large label space and two-tiered skewed label distributions. |
| Approach: | They evaluate several group-robust optimization algorithms proposed to mitigate temporal concept drift and class imbalance in document classification. |
| Outcome: | The proposed algorithms outperform sampling-based approaches to class imbalance and concept drift and lead to much better performance on minority classes. |
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification (2021.emnlp-main)
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| Challenge: | Fine-grained classification tasks involve distinguishing between classes with subtle differences between them. |
| Approach: | They analyse fine-grained text classification tasks by embedding class relationships into a contrastive objective function to help differently weigh the positives and negatives. |
| Outcome: | The proposed model outperforms previous contrastive methods on emotion classification and sentiment analysis. |