Abstract
Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and text summarization) due to their better correlation with human judgments over traditional overlap-based methods. Although PT-based methods have become the de facto standard for training grammatical error correction (GEC) systems, GEC evaluation still does not benefit from pretrained knowledge. This paper takes the first step towards understanding and improving GEC evaluation with pretraining. We first find that arbitrarily applying PT-based metrics to GEC evaluation brings unsatisfactory correlation results because of the excessive attention to inessential systems outputs (e.g., unchanged parts). To alleviate the limitation, we propose a novel GEC evaluation metric to achieve the best of both worlds, namely PT-M2 which only uses PT-based metrics to score those corrected parts. Experimental results on the CoNLL14 evaluation task show that PT-M2 significantly outperforms existing methods, achieving a new state-of-the-art result of 0.949 Pearson correlation. Further analysis reveals that PT-M2 is robust to evaluate competitive GEC systems. Source code and scripts are freely available at https://github.com/pygongnlp/PT-M2.- Anthology ID:
- 2022.emnlp-main.463
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6891–6902
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.463
- DOI:
- 10.18653/v1/2022.emnlp-main.463
- Cite (ACL):
- Peiyuan Gong, Xuebo Liu, Heyan Huang, and Min Zhang. 2022. Revisiting Grammatical Error Correction Evaluation and Beyond. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6891–6902, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Revisiting Grammatical Error Correction Evaluation and Beyond (Gong et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.emnlp-main.463.pdf