Papers with low-resource approaches
Minimally-Augmented Grammatical Error Correction (D19-55)
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| Challenge: | Existing approaches to automatic grammatical error correction require error-labelled training data to achieve their best performance. |
| Approach: | They propose an unsupervised method that generates noise from inverted spell-checkers by using a synthetic error generation method. |
| Outcome: | The proposed method outperforms the current state-of-the-art for German and Russian GEC tasks without using real error-labelled training data. |