A Coin Has Two Sides: A Novel Detector-Corrector Framework for Chinese Spelling Correction
This addresses a foundational NLP task for Chinese text processing, but it is incremental as it builds on existing detector-corrector frameworks.
The paper tackles the problem of Chinese Spelling Correction by proposing a detector-corrector framework that generates two error detection results with high precision and recall, and integrates them using feature fusion and selective masking strategies, achieving efficacy validated on mainstream datasets.
Chinese Spelling Correction (CSC) stands as a foundational Natural Language Processing (NLP) task, which primarily focuses on the correction of erroneous characters in Chinese texts. Certain existing methodologies opt to disentangle the error correction process, employing an additional error detector to pinpoint error positions. However, owing to the inherent performance limitations of error detector, precision and recall are like two sides of the coin which can not be both facing up simultaneously. Furthermore, it is also worth investigating how the error position information can be judiciously applied to assist the error correction. In this paper, we introduce a novel approach based on error detector-corrector framework. Our detector is designed to yield two error detection results, each characterized by high precision and recall. Given that the occurrence of errors is context-dependent and detection outcomes may be less precise, we incorporate the error detection results into the CSC task using an innovative feature fusion strategy and a selective masking strategy. Empirical experiments conducted on mainstream CSC datasets substantiate the efficacy of our proposed method.