Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Sumanth Doddapaneni
- sl:arxiv_num : 2107.00676
- sl:arxiv_published : 2021-07-01T18:01:46Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : A Primer on Pretrained Multilingual Language Models@en
- sl:arxiv_updated : 2021-07-01T18:01:46Z
- sl:bookmarkOf : https://arxiv.org/abs/2107.00676
- sl:creationDate : 2021-07-13
- sl:creationTime : 2021-07-13T13:33:29Z
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