基于Transformer模型的中文文本自动校对研究

Research on automatic proofreading of Chinese text based on Transformer model

提出将Transformer模型应用于中文文本自动校对领域。Transformer模型与传统的基于概率、统计、规则或引入BiLSTM的Seq2Seq模型不同,该深度学习模型通过对Seq2Seq模型进行整体结构改进,从而实现中文文本自动校对。通过使用公开数据集对不同模型进行对比实验,采用准确率、召回率与F1值作为评价指标,实验结果表明,Transformer模型相比较于其他模型,在中文文本自动校对的性能上有了大幅提升。

This paper proposes to apply Transformer model in the field of Chinese text automatic proofreading. Transformer model is different from traditional Seq2 Seq model based on probability, statistics, rules or BiLSTM. This deep learning model improves the overall structure of Seq2 Seq model to achieve automatic proofreading of Chinese text. By comparing different models with public da-ta sets and using accuracy, recall rate and F1 value as evaluation indexes, the experimental results show that Transformer model has greatly improved proofreading performance compared with other models.