Abstract
It is difficult to rank and evaluate the performance of grammatical error correction (GEC) systems, as a sentence can be rewritten in numerous correct ways. A number of GEC metrics have been used to evaluate proposed GEC systems; however, each system relies on either a comparison with one or more reference texts—in what is known as the gold standard for reference-based metrics—or a separate annotated dataset to fine-tune the reference-less metric. Reference-based systems have a low correlation with human judgement, cannot capture all the ways in which a sentence can be corrected, and require substantial work to develop a test dataset. We propose a reference-less GEC evaluation system that is strongly correlated with human judgement, solves the issues related to the use of a reference, and does not need another annotated dataset for fine-tuning. The proposed system relies solely on commonly available tools. Additionally, currently available reference-less metrics do not work properly when part of a sentence is repeated as opposed to reference-based metrics. In our proposed system, we look to address issues inherent in reference-less metrics and reference-based metrics.- Anthology ID:
- 2021.emnlp-main.239
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3009–3015
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.239
- DOI:
- 10.18653/v1/2021.emnlp-main.239
- Cite (ACL):
- Md Asadul Islam and Enrico Magnani. 2021. Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3009–3015, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Is this the end of the gold standard? A straightforward reference-less grammatical error correction metric (Islam & Magnani, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.emnlp-main.239.pdf
- Data
- GUG