Papers with CoNLL-2014 shared task
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (2023.emnlp-main)
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| Challenge: | Evaluating the performance of Grammatical Error Correction systems is a challenging task due to its subjectivity. |
| Approach: | They propose a method to evaluate GEC systems in multi-reference evaluation setting . they use consistent edit boundaries to eliminate bias caused by inconsistent edit boundaries . |
| Outcome: | The proposed evaluation metric eliminates bias caused by inconsistent edit boundaries on six English reference sets. |
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Popular GEC models use large-scale synthetic corpora or use a large number of human-designed rules. |
| Approach: | They propose a model that incorporates denoised AMR as additional knowledge to get AMRs more reliable. |
| Outcome: | The proposed model reduces training time by 32% while inference time is comparable. |