Chinese Text Error Correction (CTEC) aims to detect and correct errors in the
input text, which benefits human's daily life and various downstream tasks.
Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve
CTEC task and achieve tremendous success. However, previous approaches suffer
from issues of over-correction and under-correction, and the former is
especially conspicuous in the precision-critical CTEC task. To mitigate the
issue of overcorrection, we propose a novel model-agnostic progressive
multitask learning framework for CTEC, named ProTEC, which guides a CTEC model
to learn the task from easy to difficult. We divide CTEC task into three
sub-tasks from easy to difficult: Error Detection, Error Type Identification,
and Correction Result Generation. During the training process, ProTEC guides
the model to learn text error correction progressively by incorporating these
sub-tasks into a multi-task training objective. During the inference process,
the model completes these sub-tasks in turn to generate the correction results.
Extensive experiments and detailed analyses fully demonstrate the effectiveness
and efficiency of our proposed framework.

本文提出一种基于多任务学习的 CTEC 框架 ProTEC 来解决预训练语言模型在 CTEC 中产生的过度或欠度修改问题。该框架将 CTEC 任务分为三个子任务：错误检测、错误类型识别和更正结果生成，并通过多任务学习逐步引导模型从易到难地学习，有效地提高了 CTEC 任务的效果和效率。