【摘要】 文本中存在的表达冗余、词汇误用和内容缺失等错误会显著影响文本语义的理解,当前解决上述文本错误的纠错模型存在两个主要的问题:当前的文本纠错模型主要基于编码器-解码器框架,解码速度较慢;许多工作将错误检测和修正分离成两个任务,没有形成统一的整体.为此,提出了一种基于层次化修正框架的文本纠错模型.首先,基于预训练模型建模得到文本的多种语义表示;其次,利用文本的语义表示识别出文本中错误的位置;最后,利用层次化修正框架计算精化的修正操作并完成对错误的修正.针对公开文本纠错数据集CONLL-14进行了相关实验,结果表明本文模型比所选取的对比模型有更快的解码速度和更高的召回率.更多还原

【Abstract】 Redundant expressions, misuse of words, and missing content and other text errors can seriously affect the interpretation of text semantics.There exist two major problems with current text error correction models: The Encoder-Decoder based text error correction models have slow decoding speed; Text error detection task and text correction task are handled as two separate tasks.Hence, a text error correction model based on a hierarchical editing framework is proposed in this paper.Firstly, a variety of text semantic representations are obtained through modelling pre-trained model.Secondly, text errors are located by using these text semantic representations.Finally, on the basis of hierarchical editing framework, precise editing operations are worked out to edit the errors.Experiments on the published text error correction dataset show that the proposed model has faster decoding speed and higher recall rate than comparison models.更多还原