Abstract
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets.
Abstract (translated)
在信息时代，由于其普遍性和实际应用价值，短文本分类已获得了广泛关注。近期，图学习与对比学习相结合的技术，在解决短文本分类中的语义稀疏性和标注数据不足的挑战方面显示出巨大潜力。然而，现有的模型存在一定的局限性：它们依赖于显式的数据增强技术来生成对比视图，这会导致语义污染和噪声；此外，这些模型仅关注于从生成的视图中学习内在一致性，而忽视了其他潜在视图中的有价值的区别信息。 为了解决这些问题，我们提出了一种用于短文本分类的简单图对比学习框架（SimSTC）。我们的方法包括在多个与文本相关的组件图上执行图学习以获取多视角文本嵌入，随后直接在这些建模后的嵌入上应用对比学习。特别值得注意的是，我们的方法消除了生成对比视图所需的数据增强操作，同时仍然利用了多视图对比学习的好处。尽管该模型结构简单，但在各种数据集上的性能表现却非常出色，并且超过了大型语言模型的水平。
URL
https://arxiv.org/abs/2501.09219
