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• W3130812595 abstract "In recent studies, it has been shown that Multilingual language models underperform their monolingual counterparts. It is also a well-known fact that training and maintaining monolingual models for each language is a costly and time-consuming process. Roman Urdu is a resource-starved language used popularly on social media platforms and chat apps. In this research, we propose a novel dataset of scraped tweets containing 54M tokens and 3M sentences. Additionally, we also propose RUBERT a bilingual Roman Urdu model created by additional pretraining of English BERT. We compare its performance with a monolingual Roman Urdu BERT trained from scratch and a multilingual Roman Urdu BERT created by additional pretraining of Multilingual BERT. We show through our experiments that additional pretraining of the English BERT produces the most notable performance improvement." @default.
• W3130812595 created "2021-03-01" @default.
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• W3130812595 date "2021-02-22" @default.
• W3130812595 modified "2023-09-27" @default.
• W3130812595 title "RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning." @default.
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• W3130812595 hasPublicationYear "2021" @default.
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