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. |
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. |
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. |
STSPL-SSC: Semi-Supervised Few-Shot Short Text Clustering with Semantic text similarity Optimized Pseudo-Labels (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for obtaining task-specific labels require prior knowledge of clustering categories and uncontrollable clustering centers. |
| Approach: | They propose a framework for supervised clustering using a discrete process and a robust Contrastive Learning module. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a real-world dataset with just one label per class . the proposed framework is based on k-means clustering and a robust Contrastive Learning module . |
From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models rely on pre-trained language models, which have a maximum input sequence length of 512 tokens, and therefore have 'input length limitation'. |
| Approach: | They propose a text segmentation algorithm which guarantees to produce the optimal segmentation to address the issue of input length limitation caused by PLMs. |
| Outcome: | The proposed method improves both text and label representations on MLTC datasets, unraveling the intricate correlations between texts and labels. |
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 . |
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. |
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. |
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to augment self-training (ST) in attribute-controllable language generation are limited and limited. |
| Approach: | They propose a new ST framework that integrates self-generated pseudo text into attribute-controllable language generation. |
| Outcome: | The proposed framework can be applied to semi-supervised controllable language generation. |
LLMs Enable Bag-of-Texts Representations for Short-Text Clustering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for short text clustering require labeling and no embeddings optimization. |
| Approach: | They propose a training-free method for unsupervised short text clustering that relies less on careful selection of embedders than other methods. |
| Outcome: | The proposed method achieves comparable or superior results to state-of-the-art methods, but without embeddings optimization or prior knowledge of clusters or labels. |
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
Copied to clipboard
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |