Semi-Supervised Text Classification with Balanced Deep Representation Distributions (2021.acl-long)
Copied to clipboard
| Challenge: | Semi-Supervised Text Classification (SSTC) is a type of self-training that uses labeled and unlabeled data to perform certain applications. |
| Approach: | They propose a method to initialize a deep classifier by training over labeled texts . they then alternatively predict unlabeled texts as their pseudo-labels and train them over the mixture . |
| Outcome: | Empirical results show that the proposed method is more accurate when labeled texts are scarce. |
Similar Papers
Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies develop effective pseudo-labeling methods, but they struggle with unlabeled data that have imbalanced classes mismatched with the labeled data. |
| Approach: | They propose to use pseudo-labeling to train text classification models with few labeled data and massive unlabeled data. |
| Outcome: | Empirical results show that the proposed model outperforms state-of-the-art methods on 3 common benchmarks. |
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
Copied to clipboard
| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |
Rank-Aware Negative Training for Semi-Supervised Text Classification (2023.tacl-1)
Copied to clipboard
| Challenge: | Semi-supervised text classification-based paradigms employ the spirit of self-training, but the accuracy of pseudo-labels can be a problem in real-world scenarios. |
| Approach: | They propose a Rank-aware Negative Training framework to address SSTC in noisy label learning . they rank unlabeled texts based on evidential support from the labeled texts. |
| Outcome: | The proposed framework overcomes state-of-the-art alternatives and achieves competitive performance in other scenarios. |
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to semi-supervised text classification suffer from pseudo-label bias and error accumulation. |
| Approach: | They propose a pseudo-labeling approach to semi-supervised text classification that unifies ideas from semi-semi-supervised learning and the task of learning with noise. |
| Outcome: | The proposed approach achieves a significant improvement on benchmark datasets even in the extremely-scarce-label setting. |
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to short text clustering are prone to degenerate solutions and noisy data. |
| Approach: | They propose a model to improve robustness against imbalanced and noisy data . they propose self-adaptive optimal transport and class-wise contrastive learning . |
| Outcome: | The proposed model outperforms the state-of-the-art models on eight short text clustering datasets. |
Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to hierarchical multi-label text classification (HMTC) ignore the correlation between similar samples and introduce noise . |
| Approach: | They propose a semi-supervised method that uses a label hierarchy to bring text and label embeddings closer to each other by supervised contrastive learning. |
| Outcome: | The proposed method bridges the gap between supervised contrastive learning and HMTC by bringing text and label embeddings closer. |
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multi-label text classification is a challenging task because it requires capturing label dependencies. |
| Approach: | They propose to use distribution-balanced loss functions to solve label dependency problems in multi-label text classification by capturing label dependencies from a fixed-set of labels. |
| Outcome: | The proposed loss function addresses both the class imbalance and label linkage problems and outperforms other loss functions. |
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets (2023.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed model outperforms ten baseline models in five benchmarks and is additive to language model pretraining. |
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)
Copied to clipboard
| Challenge: | Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text. |
| Approach: | They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels. |
| Outcome: | Extensive experiments show that the proposed method can bring significant performance improvements to multiple MLTC models including state-of-the-art pretrained and non-pretrained ones. |
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. |
| Approach: | They propose an explicit-agmented-generativ-e framework with distribution modification for hierarchical text classification. |
| Outcome: | The proposed framework improves on the initial distributions of tail classes and avoids misinterpreting predictions on unbalanced data. |