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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 Cross-lingual Transfer Learning for Grammatical Error Correction ... The 28th International Conference on Computational Linguistics 2020; Imankulova, Aizhan; Kaneko, Masahiro; Katsumata, Satoru; Komachi, Mamoru; Yamashita, Ikumi. - : Underline Science Inc., 2020 Abstract: In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks. Many languages lack the resources required to train GEC models. Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks. However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident. Therefore, we investigate cross-lingual transfer learning methods for GEC. Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC. We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors. ... Keyword: Computer and Information Science; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/x8gt-2w93 https://underline.io/lecture/6153-cross-lingual-transfer-learning-for-grammatical-error-correction BASE Hide details

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