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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.687.pdf
Data
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