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. |
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| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
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| Challenge: | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. |
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| Challenge: | grammatical error correction is an important NLP task that is usually solved with autoregressive sequence-to-sequence models. |
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