Papers with grammatical error correction task
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. |
Bidirectional Transformer Reranker for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Pre-trained seq2seq models suffer from a prediction bias due to their unidirectional decoding. |
| Approach: | They propose a bidirectional Transformer reranker that re-estimates the probability of each candidate sentence generated by pre-trained seq2seq models. |
| Outcome: | The proposed model improves on the original model and gives a 59.52 GLEU score on the JFLEG corpus. |
Multi-Perspective Document Revision (2022.coling-1)
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| Challenge: | a novel document revision task that revises multiple perspectives is proposed . grammatical error correction tasks have been studied in the natural language processing field . |
| Approach: | They propose a Japanese multi-perspective document revision task that revises multiple perspectives to improve the readability and clarity of a document. |
| Outcome: | The proposed model can be used to improve the readability and clarity of a document. |