Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag space and 2) it is multi-label. Though a retrieval approach is reportedly good at low-resource scenarios, there have been fewer efforts to directly address the data scarcity problem. To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification. Our main contributions are as follows: 1) we leverage transfer learning from question-answering datasets, and 2) we propose a simple but effective data augmentation method introducing cross-encoder style texts to a bi-encoder architecture for more efficient inference. An extensive set of experiments shows that our proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models.

翻译：教育领域的文本分类通常称为自动标注，是指为教学资源（如习题和教材）自动分配相关标签的过程。然而，自动标注面临数据稀缺问题，其根源在于两大挑战：1）标签空间庞大，2）属于多标签分类任务。尽管检索式方法在低资源场景下表现优异，但直接针对数据稀缺问题的研究仍较少。为缓解这些问题，本文提出一种新颖的检索式方法CEAA，可在教育文本分类中实现高效学习。主要贡献如下：1）利用问答数据集进行迁移学习；2）提出一种简单而有效的数据增强方法，将交叉编码风格的文本引入双编码器架构以实现更高效的推理。大量实验表明，与现有最优模型相比，所提方法在多标签场景及低资源标签上均表现高效。