@inproceedings{ingolfsdottir-etal-2023-byte,
    title = "Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora",
    author = "Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja  and
      Ragnarsson, Petur  and
      J{\'o}nsson, Haukur  and
      Simonarson, Haukur  and
      Thorsteinsson, Vilhjalmur  and
      Sn{\ae}bjarnarson, V{\'e}steinn",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.402",
    doi = "10.18653/v1/2023.acl-long.402",
    pages = "7299--7316",
    abstract = "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 error 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, and in particular to morphologically rich ones.",
}

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 error 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, and in particular to morphologically rich ones.
Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja Ragnarsson, Petur J{\'o}nsson, Haukur Simonarson, Haukur Thorsteinsson, Vilhjalmur Sn{\ae}bjarnarson, V{\'e}steinn
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora