Detection-Correction Structure via General Language Model for Grammatical Error Correction
Wei Li, Houfeng Wang · 2024
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 detectioncorrection structure in LLMs, suggesting a promising direction for GEC.