كانت هناك جهود في التعلم عبر التحويلات عبر اللغات لمختلف المهام.نقدم نهجا باستخدام طريقة تكبير البيانات الاستكمالية، والابتزاز، لتحسين تعميم النماذج من أجل وضع علامات جزء من الكلام المدربين على لغة مصدر، وتحسين أدائها على اللغات المستهدفة غير المرئية.من خلال التجارب على عشرة لغات مع الهياكل المتنوعة وجذور اللغة، نطرح تطبيقها لتطبيقها على المهام المتبادلة الصفرية الصفرية.
There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.
References used
https://aclanthology.org/
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