  Chinese Text Error Correction (CTEC) aims to detect and correct errors in the
input text, which benefits human daily life and various downstream tasks.
Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve
CTEC. Although PLMs have achieved remarkable success in CTEC, we argue that
previous studies still overlook the importance of human thinking patterns. To
enhance the development of PLMs for CTEC, inspired by humans' daily
error-correcting behavior, we propose a novel model-agnostic progressive
learning framework, named ProTEC, which guides PLMs-based CTEC models to learn
to correct like humans. During the training process, ProTEC guides the model to
learn text error correction by incorporating these sub-tasks into a progressive
paradigm. During the inference process, the model completes these sub-tasks in
turn to generate the correction results. Extensive experiments and detailed
analyses demonstrate the effectiveness and efficiency of our proposed
model-agnostic ProTEC framework.
