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. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |
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| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
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| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
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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: | Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient. |
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| Challenge: | Grammatical error correction (GEC) is a critical task in natural language processing . most approaches create language-specific models, limiting their multilingual applicability. |
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Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)
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| Challenge: | Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available. |
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Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation (N18-2)
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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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Grammatical Error Correction through Round-Trip Machine Translation (2023.findings-eacl)
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| Challenge: | A decade ago the idea of using round-trip MT to guide grammatical error correction was not feasible due to the low quality of MT systems of the day. |
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Type-Driven Multi-Turn Corrections for Grammatical Error Correction (2022.findings-acl)
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| Challenge: | Existing studies focus on data augmentation to combat exposure bias . but data augmented models lack the ability to recognize the procedure of gradual corrections . |
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