Automatical sampling with heterogeneous corpora for grammatical error correction
Abstract Thanks to the strong representation capability of the pre-trained language models, supervised grammatical error correction has achieved promising performance. However, traditional model training depends significantly on the large scale of similar distributed samples. The model performance d...
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| Главные авторы: | , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Springer
2024-11-01
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| Серии: | Complex & Intelligent Systems |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/s40747-024-01653-3 |
| Метки: |
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