基于多层级语言特征融合的中文文本可读性分级模型

Chinese Text Readability Grading via Multi-level Linguistic Feature Fusion

中文文本可读性分级任务的目标是将中文文本按照其可读性划分到相应的难度等级。近年来研究表明,语言特征与深度语义特征在表征文章难度上体现出互补性。但已有的工作仅对两类特征进行浅层融合,尚未考虑将语言特征和深度模型进行深层、多层级融合。因此,该文在基于BERT的传统文本可读性分级模型的基础上,设计多层级语言特征融合方法,考虑到不同语言特征和网络层结构的交互,将汉字、词汇和语法的语言特征与模型的嵌入层和自注意力层进行融合。实验结果显示,该文的方法在中文文本可读性分级任务上的效果超过了所有基线模型,并在测试集上达到94.2%的准确率。

The goal of Chinese text readability grading task is to classify Chinese texts into the appropriate difficulty levels for readers.Recent studies have shown that linguistic features and deep semantic features are complementary in characterizing the difficulty of text.However,existing work only performed shallow fusion of these two types of features,and deep,multi-level fusion has not been considered.Therefore,this paper develops a multi-level linguistic feature fusion strategy based on the traditional text readability grading model on BERT.Specifically,considering the interaction of different linguistic features and network layer structures,this paper fused the linguistic features of characters,words and grammar in the embedding layer as well as the self-attention layer.The experimental results show that the proposed method outperforms all baseline models and by 94.2%accuracy.