Class-Aware Contrastive Optimization for Imbalanced Text Classification

• URL: http://arxiv.org/abs/2410.22197v1
• Date: Tue, 29 Oct 2024 16:34:08 GMT
• Title: Class-Aware Contrastive Optimization for Imbalanced Text Classification
• Authors: Grigorii Khvatskii, Nuno Moniz, Khoa Doan, Nitesh V Chawla,
• Abstract summary: We show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks. Our proposal demonstrates a notable increase in performance across a wide variety of text datasets.
• Score: 19.537124894139833
• License:
• Abstract: The unique characteristics of text data make classification tasks a complex problem. Advances in unsupervised and semi-supervised learning and autoencoder architectures addressed several challenges. However, they still struggle with imbalanced text classification tasks, a common scenario in real-world applications, demonstrating a tendency to produce embeddings with unfavorable properties, such as class overlap. In this paper, we show that leveraging class-aware contrastive optimization combined with denoising autoencoders can successfully tackle imbalanced text classification tasks, achieving better performance than the current state-of-the-art. Concretely, our proposal combines reconstruction loss with contrastive class separation in the embedding space, allowing a better balance between the truthfulness of the generated embeddings and the model's ability to separate different classes. Compared with an extensive set of traditional and state-of-the-art competing methods, our proposal demonstrates a notable increase in performance across a wide variety of text datasets.

Related papers

• Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification [11.072083437769093]
  We propose a novel model named SharpReCL for imbalanced text classification tasks. Our model even outperforms popular large language models across several datasets.
  arXiv  Detail & Related papers  (2024-05-19T11:33:49Z)
• ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification [40.808421462004866]
  We propose ContrastNet to tackle both discriminative representation and overfitting problems in few-shot text classification. Experiments on 8 few-shot text classification datasets show that ContrastNet outperforms the current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-16T08:22:17Z)
• Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation [40.09476732999614]
  Mask proposal models have significantly improved the performance of zero-shot semantic segmentation. The use of a background' embedding during training in these methods is problematic as the resulting model tends to over-learn and assign all unseen classes as the background class instead of their correct labels. This paper proposes novel class enhancement losses to bypass the use of the background embbedding during training, and simultaneously exploit the semantic relationship between text embeddings and mask proposals by ranking the similarity scores.
  arXiv  Detail & Related papers  (2023-01-18T06:55:02Z)
• Conditional Supervised Contrastive Learning for Fair Text Classification [59.813422435604025]
  We study learning fair representations that satisfy a notion of fairness known as equalized odds for text classification via contrastive learning. Specifically, we first theoretically analyze the connections between learning representations with a fairness constraint and conditional supervised contrastive objectives.
  arXiv  Detail & Related papers  (2022-05-23T17:38:30Z)
• Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing [88.76112371510999]
  Federated learning is a prime candidate for distributed machine learning at the network edge. Existing algorithms face issues with slow convergence and/or robustness of performance. We propose a contextual aggregation scheme that achieves the optimal context-dependent bound on loss reduction.
  arXiv  Detail & Related papers  (2022-03-23T21:42:31Z)
• CLLD: Contrastive Learning with Label Distance for Text Classificatioin [0.6299766708197883]
  We propose Contrastive Learning with Label Distance (CLLD) for learning contrastive classes. CLLD ensures the flexibility within the subtle differences that lead to different label assignments. Our experiments suggest that the learned label distance relieve the adversarial nature of interclasses.
  arXiv  Detail & Related papers  (2021-10-25T07:07:14Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Active Hybrid Classification [79.02441914023811]
  This paper shows how crowd and machines can support each other in tackling classification problems. We propose an architecture that orchestrates active learning and crowd classification and combines them in a virtuous cycle.
  arXiv  Detail & Related papers  (2021-01-21T21:09:07Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)
• Long-Tailed Recognition Using Class-Balanced Experts [128.73438243408393]
  We propose an ensemble of class-balanced experts that combines the strength of diverse classifiers. Our ensemble of class-balanced experts reaches results close to state-of-the-art and an extended ensemble establishes a new state-of-the-art on two benchmarks for long-tailed recognition.
  arXiv  Detail & Related papers  (2020-04-07T20:57:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.