用对比学习提升不平衡文本分类效果，避免类别嵌入重叠。
Class-Aware Contrastive Optimization for Imbalanced Text Classification
- 结合去噪自编码器与类别感知对比优化，分离不同类别的嵌入。
- 在多个文本数据集上超越现有最先进方法，提升分类性能。
- 适合处理真实场景中类别不均衡的文本分类任务。
文本数据的特性使得分类任务复杂化。尽管无监督和半监督学习以及自编码器架构解决了部分挑战，但在不平衡文本分类任务中仍表现不佳，常导致嵌入空间中类别重叠等问题。本文提出，结合类别感知对比优化与去噪自编码器可有效应对不平衡文本分类问题，性能优于当前最先进方法。具体而言，该方法在嵌入空间中同时优化重建损失与类别对比分离，实现生成嵌入的真实性与类别区分能力之间的更好平衡。在多种主流文本数据集上的实验表明，该方法相比众多传统及前沿方法均有显著性能提升。
原文摘要 · Abstract (English)
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.
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