Detection-Correction Structure via General Language Model for Grammatical Error Correction
<|reference_start|>Detection-Correction Structure via General Language Model for Grammatical Error Correction: Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction. However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model. Moreover, the exploration of the detection-correction paradigm by large language models (LLMs) remains underdeveloped. This paper introduces an integrated detection-correction structure, named DeCoGLM, based on the General Language Model (GLM). The detection phase employs a fault-tolerant detection template, while the correction phase leverages autoregressive mask infilling for localized error correction. Through the strategic organization of input tokens and modification of attention masks, we facilitate multi-task learning within a single model. Our model demonstrates competitive performance against the state-of-the-art models on English and Chinese GEC datasets. Further experiments present the effectiveness of the detection-correction structure in LLMs, suggesting a promising direction for GEC.<|reference_end|>
@article{li2024detection-correction,
    title={Detection-Correction Structure via General Language Model for
  Grammatical Error Correction},
    author={Wei Li, Houfeng Wang},
    journal={arXiv preprint arXiv:2405.17804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17804},
    primaryClass={cs.CL}
}