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1
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
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2
Neural Combinatory Constituency Parsing ...
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3
Cross-lingual Transfer Learning for Grammatical Error Correction ...
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
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Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation ...
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