Abstract
The explosion of textual data has made manual document classification increasingly challenging. To address this, we introduce a robust, efficient domain-agnostic generative model framework for multi-label text classification. Instead of treating labels as mere atomic symbols, our approach utilizes predefined label descriptions and is trained to generate these descriptions based on the input text. During inference, the generated descriptions are matched to the pre-defined labels using a finetuned sentence transformer. We integrate this with a dual-objective loss function, combining cross-entropy loss and cosine similarity of the generated sentences with the predefined target descriptions, ensuring both semantic alignment and accuracy. Our proposed model LAGAMC stands out for its parameter efficiency and versatility across diverse datasets, making it well-suited for practical applications. We demonstrate the effectiveness of our proposed model by achieving new state-of-the-art performances across all evaluated datasets, surpassing several strong baselines. We achieve improvements of 13.94% in Micro-F1 and 24.85% in Macro-F1 compared to the closest baseline across all datasets.
Abstract (translated)
文本数据的爆炸式增长使得手动文档分类变得越来越具有挑战性。为了解决这一问题，我们引入了一个健壮、高效且领域无关的生成模型框架，用于多标签文本分类。与将标签视为单纯原子符号的做法不同，我们的方法利用预定义的标签描述，并训练模型根据输入文本生成这些描述。在推理阶段，通过使用经过微调的句子变换器将生成的描述匹配到预先定义的标签上。我们集成了一个双重目标损失函数，结合了交叉熵损失和生成句与预定义的目标描述之间的余弦相似度，以确保语义的一致性和准确性。 我们的模型LAGAMC（Label Description Guided Attention Model for Multi-label Classification）以其参数效率和跨多种数据集的通用性而脱颖而出，非常适合实际应用。我们通过在所有评估的数据集中实现新的最先进性能，并超越了多个强大的基线，证明了所提模型的有效性。与最近的最佳方法相比，我们在所有数据集上分别实现了微平均F1分数提高了13.94%，宏平均F1分数提高了24.85%的成绩。
URL
https://arxiv.org/abs/2506.06806
