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Grammatical error correction (GEC) is the task of correcting typos, spelling,punctuation and grammatical issues in text. Approaching the problem as asequence-to-sequence task, we compare the use of a common subword unitvocabulary and byte-level encoding. Initial synthetic training data is createdusing an error-generating pipeline, and used for finetuning two subword-levelmodels and one byte-level model. Models are then finetuned further onhand-corrected error corpora, including texts written by children, universitystudents, dyslexic and second-language writers, and evaluated over differenterror types and origins. We show that a byte-level model enables highercorrection quality than a subword approach, not only for simple spellingerrors, but also for more complex semantic, stylistic and grammatical issues.In particular, initial training on synthetic corpora followed by finetuning ona relatively small parallel corpus of real-world errors helps the byte-levelmodel correct a wide range of commonly occurring errors. Our experiments arerun for the Icelandic language but should hold for other similar languages,particularly morphologically rich ones.

Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora