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Register a new userImproving the Performance of UDify with Linguistic Typology Knowledge
تحسين أداء Udify مع المعرفة النمطية اللغوية
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UDIFY هو محلل التبعية اللاإرائي لغرفة التبعية اللغوية التي يتم تدريبها على كوربوس متعدد الألوان من 75 لغة.يتيح هذا النمذجة متعددة اللغات النموذج من التعميم على اللغات غير المعروفة / الأقل شهرة، مما يؤدي إلى تحسين الأداء على لغات الموارد المنخفضة.في هذا العمل، استخدمنا المعرفة النموذجية اللغوية المتاحة في قاعدة بيانات أوريل، لتحسين قدرة النقل المتبقية من udify أكثر من ذلك.
UDify is the state-of-the-art language-agnostic dependency parser which is trained on a polyglot corpus of 75 languages. This multilingual modeling enables the model to generalize over unknown/lesser-known languages, thus leading to improved performance on low-resource languages. In this work we used linguistic typology knowledge available in URIEL database, to improve the cross-lingual transferring ability of UDify even further.
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Network coding isone of the important researches in multi-hop wireless networks domain
and it widely participates in improving the performance of these networks, since it benefits from
the broadcasting nature of transmission processes to transmit m
ore than single packet in one
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verify the improvement that network coding presents to theperformance of multi-hops wireless
Ad-hoc networks, and to study the accelerating of research process for coding chances through
constructing a virtual queues according to the packets flows that pass the node, and applying
affective manner to manage this queues.
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