Abstract
Automatic evaluation of grammatical error correction (GEC) is essential in developing useful GEC systems. Existing methods for automatic evaluation require multiple reference sentences or manual scores. However, such resources are expensive, thereby hindering automatic evaluation for various domains and correction styles. This paper proposes an Impact-based Metric for GEC using PARAllel data, IMPARA, which utilizes correction impacts computed by parallel data comprising pairs of grammatical/ungrammatical sentences. As parallel data is cheaper than manually assessing evaluation scores, IMPARA can reduce the cost of data creation for automatic evaluation. Correlations between IMPARA and human scores indicate that IMPARA is comparable or better than existing evaluation methods. Furthermore, we find that IMPARA can perform evaluations that fit different domains and correction styles trained on various parallel data.- Anthology ID:
- 2022.coling-1.316
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3578–3588
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.316
- DOI:
- Cite (ACL):
- Koki Maeda, Masahiro Kaneko, and Naoaki Okazaki. 2022. IMPARA: Impact-Based Metric for GEC Using Parallel Data. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3578–3588, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- IMPARA: Impact-Based Metric for GEC Using Parallel Data (Maeda et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.316.pdf
- Data
- FCE, JFLEG