Chinese Semantic Error

Chinese Semantic Error (CSE) research focuses on identifying and correcting errors in text that stem from flawed meaning, rather than just spelling or grammar. Current efforts concentrate on developing datasets for CSE recognition and correction, employing techniques like syntax-aware models and leveraging pre-trained language models (e.g., BERT-based architectures) enhanced with semantic features to improve accuracy. Addressing CSE is crucial for advancing natural language processing in Chinese, improving the reliability of machine translation, text summarization, and other applications that depend on accurate semantic understanding.