Improving Grammatical Error Correction by Correction Acceptability Discrimination (2024.lrec-main)
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| Challenge: | Existing Grammatical Error Correction (GEC) methods overlook the assessment of sentence-level syntax and semantics in the corrected sentence. |
| Approach: | They propose a correction acceptance discrimination task to assess sentence-level syntax and semantics in corrected sentences and a pipeline method to remove invalid corrections. |
| Outcome: | The proposed method improves F0.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set. |
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| Challenge: | Experimental results show that adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines. |
| Approach: | They propose an adversarial approach to Grammatical Error Correction using a transformer-based model and a sentence-pair classification model. |
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A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction (2020.findings-emnlp)
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| Challenge: | Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets. |
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CxGGEC: Construction-Guided Grammatical Error Correction (2025.acl-long)
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| Challenge: | Current GEC methods rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. |
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| Challenge: | Popular GEC models use large-scale synthetic corpora or use a large number of human-designed rules. |
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Grammatical Error Correction: Are We There Yet? (2022.coling-1)
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| Challenge: | grammatical error correction (GEC) systems outperform humans on the CoNLL-2014 test set, but there are still classes of errors that they fail to correct. |
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| Challenge: | errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems. |
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Evaluation of Really Good Grammatical Error Correction (2024.lrec-main)
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| Challenge: | emergence of large language models has highlighted the shortcomings of evaluation methods . evaluators often use grammatical error correction (GEC) to correct language errors at multiple levels . |
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LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models. |
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SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser (2022.emnlp-main)
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| Challenge: | Existing approaches to grammatical error correction are unreliable when processing ungrammatically . a new approach is proposed that incorporates dependency syntactic information into the encoder part of GEC models. |
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Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
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