Grammatical Error Correction
Overview
Grammatical error correction studies how to identify and fix errors in written or spoken language while preserving the writer’s intended meaning. Common approaches include neural sequence-to-sequence models trained on parallel or synthetically corrupted text, as well as large language models guided by prompts, retrieval, fine-tuning, or reinforcement learning. Systems are tested on benchmarks such as CoNLL-2014 and BEA-2019, with growing work on multilingual, low-resource, spoken, and domain-specific settings. A central challenge is balancing precision and minimal edits against fluency: systems must avoid over-correction, handle valid alternatives that differ from a reference, and provide reliable evaluation and useful feedback.
Latest papers
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