Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora
This work addresses grammatical error correction for diverse groups like children, students, and second-language writers, but it is incremental as it builds on existing sequence-to-sequence methods with a focus on encoding and data strategies.
The paper tackled grammatical error correction (GEC) by comparing subword and byte-level encoding models, showing that a byte-level model achieves higher correction quality across various error types, including semantic and grammatical issues, with improvements demonstrated through experiments in Icelandic.
Grammatical error correction (GEC) is the task of correcting typos, spelling, punctuation and grammatical issues in text. Approaching the problem as a sequence-to-sequence task, we compare the use of a common subword unit vocabulary and byte-level encoding. Initial synthetic training data is created using an error-generating pipeline, and used for finetuning two subword-level models and one byte-level model. Models are then finetuned further on hand-corrected error corpora, including texts written by children, university students, dyslexic and second-language writers, and evaluated over different error types and origins. We show that a byte-level model enables higher correction quality than a subword approach, not only for simple spelling errors, but also for more complex semantic, stylistic and grammatical issues. In particular, initial training on synthetic corpora followed by finetuning on a relatively small parallel corpus of real-world errors helps the byte-level model correct a wide range of commonly occurring errors. Our experiments are run for the Icelandic language but should hold for other similar languages, particularly morphologically rich ones.