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• W3180299705 abstract "Multilingual Language Models (MLLMs) such as mBERT, XLM, XLM-R, textit{etc.} have emerged as a viable option for bringing the power of pretraining to a large number of languages. Given their success in zero shot transfer learning, there has emerged a large body of work in (i) building bigger MLLMs covering a large number of languages (ii) creating exhaustive benchmarks covering a wider variety of tasks and languages for evaluating MLLMs (iii) analysing the performance of MLLMs on monolingual, zero shot crosslingual and bilingual tasks (iv) understanding the universal language patterns (if any) learnt by MLLMs and (v) augmenting the (often) limited capacity of MLLMs to improve their performance on seen or even unseen languages. In this survey, we review the existing literature covering the above broad areas of research pertaining to MLLMs. Based on our survey, we recommend some promising directions of future research." @default.
• W3180299705 created "2021-07-19" @default.
• W3180299705 creator A5030945392 @default.
• W3180299705 creator A5031718651 @default.
• W3180299705 creator A5050036814 @default.
• W3180299705 creator A5052909911 @default.
• W3180299705 creator A5078774993 @default.
• W3180299705 date "2021-07-01" @default.
• W3180299705 modified "2023-09-24" @default.
• W3180299705 title "A Primer on Pretrained Multilingual Language Models" @default.
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