MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction (2023.findings-emnlp)
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| Challenge: | Various data augmentation strategies have been proposed to improve GEC models . high-quality parallel data for GEC is not as widely available . |
| Approach: | They propose a data augmentation approach that strategically augments real data by generating pseudo data. |
| Outcome: | The proposed approach significantly improves GEC models on English and Chinese datasets. |
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| Challenge: | Experimental results show that data augmentation improves accuracy over strong baselines. |
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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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| Challenge: | Recent studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. |
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| Challenge: | incorporating pseudo data in the training of grammatical error correction models has been a key factor in improving performance of such models. |
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Data Weighted Training Strategies for Grammatical Error Correction (2020.tacl-1)
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| Challenge: | Recent advances in the task of Grammatical Error Correction (GEC) have been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task. |
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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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| Challenge: | Existing approaches to automatic grammatical error correction require error-labelled training data to achieve their best performance. |
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| Challenge: | In recent years, natural language inference has been an emerging research area . a new data augmentation technique is used to augment pre-trained language models . |
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