Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models (2023.findings-acl)
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| Challenge: | Autoregressive models assign low probabilities to tokens that need corrections . grammatical error correction (GEC) is widely applied to natural language processing tasks . |
| Approach: | They propose to use a non-autoregressive model as an auxiliary model to train GEC models to correct grammatical errors in sentences. |
| Outcome: | The proposed method outperforms baselines on English and Chinese GEC tasks significantly. |
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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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| 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: | Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets. |
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Do Grammatical Error Correction Models Realize Grammatical Generalization? (2021.findings-acl)
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| Challenge: | Existing models for grammatical error correction use pseudo data, but they are inconvenient for realworld deployment due to large amounts of training data. |
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Grammatical Error Correction with Contrastive Learning in Low Error Density Domains (2021.findings-emnlp)
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| Challenge: | grammatical error correction (GEC) is a text generation task . performance on low error density domains where texts written by native speakers can be improved. |
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Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
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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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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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