Abstract
Grammatical Error Correction (GEC) is the task of correcting errorful sentences into grammatically correct, semantically consistent, and coherent sentences. Popular GEC models either use large-scale synthetic corpora or use a large number of human-designed rules. The former is costly to train, while the latter requires quite a lot of human expertise. In recent years, AMR, a semantic representation framework, has been widely used by many natural language tasks due to its completeness and flexibility. A non-negligible concern is that AMRs of grammatically incorrect sentences may not be exactly reliable. In this paper, we propose the AMR-GEC, a seq-to-seq model that incorporates denoised AMR as additional knowledge. Specifically, We design a semantic aggregated GEC model and explore denoising methods to get AMRs more reliable. Experiments on the BEA-2019 shared task and the CoNLL-2014 shared task have shown that AMR-GEC performs comparably to a set of strong baselines with a large number of synthetic data. Compared with the T5 model with synthetic data, AMR-GEC can reduce the training time by 32% while inference time is comparable. To the best of our knowledge, we are the first to incorporate AMR for grammatical error correction.- Anthology ID:
- 2023.findings-acl.449
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7180–7188
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.449
- DOI:
- 10.18653/v1/2023.findings-acl.449
- Cite (ACL):
- Hejing Cao and Dongyan Zhao. 2023. Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 7180–7188, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction (Cao & Zhao, Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.449.pdf