Research Paper#Natural Language Processing, Chinese Spelling Correction, Reinforcement Learning, LLM🔬 ResearchAnalyzed: Jan 3, 2026 16:53
CEC-Zero: Zero-Supervision Chinese Spelling Correction
Analysis
This paper introduces a novel zero-supervision approach, CEC-Zero, for Chinese Spelling Correction (CSC) using reinforcement learning. It addresses the limitations of existing methods, particularly the reliance on costly annotations and lack of robustness to novel errors. The core innovation lies in the self-generated rewards based on semantic similarity and candidate agreement, allowing LLMs to correct their own mistakes. The paper's significance lies in its potential to improve the scalability and robustness of CSC systems, especially in real-world noisy text environments.
Key Takeaways
- •CEC-Zero is a zero-supervision reinforcement learning framework for Chinese Spelling Correction.
- •It uses self-generated rewards based on semantic similarity and candidate agreement.
- •It outperforms supervised baselines and LLM fine-tunes on multiple benchmarks.
- •It establishes a label-free paradigm for robust and scalable CSC.
Reference
“CEC-Zero outperforms supervised baselines by 10--13 F$_1$ points and strong LLM fine-tunes by 5--8 points across 9 benchmarks.”