Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
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| Challenge: | Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data. |
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Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020.acl-main)
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| Challenge: | Existing methods for incorporating a masked language model into an EncDec model have potential drawbacks when applied to GEC. |
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No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
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Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation (2023.emnlp-main)
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| Challenge: | Existing studies on grammatical error correction (GEC) in morphologically rich languages have been limited due to data scarcity and language complexity. |
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Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model (2020.aacl-main)
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| Challenge: | Strong pretraining approaches for grammatical error correction require extensive use of a pseudo-parallel corpus. |
| Approach: | They propose to use bidirectional and auto-regressive transformers as a generic pretrained encoder-decoder model for grammatical error correction (GEC) they find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC. |
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Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (N18-2)
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| Challenge: | Currently, most effective GEC systems are based on phrase-based statistical machine translation. |
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