Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora
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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 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.
Original language | English |
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Title of host publication | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics : Long Papers |
Publisher | Association for Computational Linguistics (ACL) |
Publication date | 2023 |
Pages | 7299-7316 |
ISBN (Electronic) | 9781959429722 |
Publication status | Published - 2023 |
Event | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada Duration: 9 Jul 2023 → 14 Jul 2023 |
Conference
Conference | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
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Land | Canada |
By | Toronto |
Periode | 09/07/2023 → 14/07/2023 |
Sponsor | Bloomberg Engineering, et al., Google Research, Liveperson, Meta, Microsoft |
Bibliographical note
Funding Information:
We thank the Icelandic Language Technology Program (Nikulásdóttir et al., 2020). It has enabled the authors to focus on work in Icelandic NLP. Snæb-jarnarson was partially funded by the Pioneer Centre for AI, DNRF grant number P1, during the time of this work. Finally, we thank the anonymous reviewers for their helpful feedback.
Funding Information:
We thank the Icelandic Language Technology Program (Nikulásdóttir et al., 2020). It has enabled the authors to focus on work in Icelandic NLP. Snæbjarnarson was partially funded by the Pioneer Centre for AI, DNRF grant number P1, during the time of this work. Finally, we thank the anonymous reviewers for their helpful feedback.
Publisher Copyright:
© 2023 Association for Computational Linguistics.
Links
- https://aclanthology.org/2023.acl-long.402/
Final published version
ID: 371185212