Improving Grammatical Error Correction with Data Augmentation by Editing Latent Representation (2020.coling-main)
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| Challenge: | Existing methods for enhancing grammatical error correction use noise to generate tokens . existing methods only generate sentences with limited error types, which leads to lack of diversity of generated errors. |
| Approach: | They propose a data augmentation method that can apply noise to latent representations of a sentence to generate synthetic samples with various error types. |
| Outcome: | The proposed method improves performance and robustness of existing models on public benchmarks and on FCE benchmarks. |
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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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| 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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