Chinese Text

Research on Chinese text focuses on automatically detecting and correcting errors, encompassing spelling, grammar, and—increasingly—semantic issues. Current efforts leverage large language models (LLMs) and encoder-decoder architectures, often enhanced by incorporating human-like progressive learning strategies or adversarial multi-task learning to improve accuracy, particularly for complex semantic errors. This work is significant because accurate and efficient Chinese text correction is crucial for improving the quality of digital content, facilitating natural language processing applications, and advancing our understanding of language processing itself.