Papers with Transformer-based pretrained sequence-to-sequence models
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation (2023.emnlp-main)
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| Challenge: | Existing studies on grammatical error correction (GEC) in morphologically rich languages have been limited due to data scarcity and language complexity. |
| Approach: | They propose to use Arabic GEC to improve performance across three datasets . they define Arabic grammatical error detection task as auxiliary input . |
| Outcome: | The proposed models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. |