Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
Zheng Yuan, Shiva Taslimipoor, Christopher Davis, Christopher Bryant
Abstract
In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.- Anthology ID:
- 2021.emnlp-main.687
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8722–8736
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.687
- DOI:
- 10.18653/v1/2021.emnlp-main.687
- Cite (ACL):
- Zheng Yuan, Shiva Taslimipoor, Christopher Davis, and Christopher Bryant. 2021. Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8722–8736, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (Yuan et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.emnlp-main.687.pdf
- Data
- FCE