Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors (2022.findings-acl)
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| Challenge: | a language model-based error detection method can learn errors with a small training sample. |
| Approach: | They propose a language model-based method for grammatical error detection with feedback comments. |
| Outcome: | The proposed method can learn errors with a little training data and improve recall faster than non-language models. |
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| Challenge: | Existing methods for incorporating a masked language model into an EncDec model have potential drawbacks when applied to GEC. |
| Approach: | They propose to incorporate a pre-trained masked language model (MLM) into an encoder-decoder model for grammatical error correction. |
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| Challenge: | Recent studies have shown that pre-trained models improve performance on downstream tasks. |
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Do Grammatical Error Correction Models Realize Grammatical Generalization? (2021.findings-acl)
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GEE! Grammar Error Explanation with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing grammatical error correction tools do not provide natural language explanations of errors . a system needs to provide one-sentence explanations for each grammamatical errors in a pair of erroneous and corrected sentences. |
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Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (D18-1)
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| Challenge: | grammatical error correction is a labor-intensive task that requires large amounts of training data. |
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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. |
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Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems (2021.emnlp-main)
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| Challenge: | a multi-class grammatical error detection system can be used to improve grammamatical errors correction (GEC) for English. |
| Approach: | They develop a multi-class grammatical error detection system based on pre-trained ELECTRA and extend it to multi-Class detection using different error type tagsets. |
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