TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)
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| Challenge: | Experimental results show that data augmentation improves accuracy over strong baselines. |
| Approach: | They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts . |
| Outcome: | The proposed method improves correction accuracy over strong baselines on four GEC benchmarks. |
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