论文标题
通过意图信息提高文本分类精度
Improve Text Classification Accuracy with Intent Information
论文作者
论文摘要
文本分类是面向任务的对话系统的核心组成部分，吸引了研究和行业社区的持续研究，并取得了巨大的进步。但是，现有方法不考虑使用标签信息，这可能会在某些令牌感知的情况下削弱文本分类系统的性能。为了解决该问题，在本文中，我们介绍了标签信息作为文本分类任务的标签嵌入，并在基准数据集上实现出色的性能。
Text classification, a core component of task-oriented dialogue systems, attracts continuous research from both the research and industry community, and has resulted in tremendous progress. However, existing method does not consider the use of label information, which may weaken the performance of text classification systems in some token-aware scenarios. To address the problem, in this paper, we introduce the use of label information as label embedding for the task of text classification and achieve remarkable performance on benchmark dataset.
