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Chinese Text Error Correction (CTEC) aims to detect and correct errors in theinput text, which benefits human daily life and various downstream tasks.Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolveCTEC. Although PLMs have achieved remarkable success in CTEC, we argue thatprevious studies still overlook the importance of human thinking patterns. Toenhance the development of PLMs for CTEC, inspired by humans' dailyerror-correcting behavior, we propose a novel model-agnostic progressivelearning framework, named ProTEC, which guides PLMs-based CTEC models to learnto correct like humans. During the training process, ProTEC guides the model tolearn text error correction by incorporating these sub-tasks into a progressiveparadigm. During the inference process, the model completes these sub-tasks inturn to generate the correction results. Extensive experiments and detailedanalyses demonstrate the effectiveness and efficiency of our proposedmodel-agnostic ProTEC framework.

Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction