ALBERT: A Lite BERT for Self-supervised Learning of Language Representations: Difference between revisions
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==Introduction== | ==Introduction== | ||
In this paper, the authors have made some changes to the BERT model and the result is ALBERT, a model that out-performs BERT on GLUE, SQuAD, and RACE benchmarks. The important point is that ALBERT has fewer number of parameters than BERT-large, but still it gets better results. The above mentioned changes are Factorized embedding parameterization and Cross-layer parameter sharing which are two methods of parameter reduction. They also introduced a new loss function and replaced it with one of the loss functions being used in BERT (i.e. NSP). The last change is removing dropout from the model. | |||
== Motivation == | == Motivation == |
Revision as of 19:28, 2 November 2020
Presented by
Maziar Dadbin
Introduction
In this paper, the authors have made some changes to the BERT model and the result is ALBERT, a model that out-performs BERT on GLUE, SQuAD, and RACE benchmarks. The important point is that ALBERT has fewer number of parameters than BERT-large, but still it gets better results. The above mentioned changes are Factorized embedding parameterization and Cross-layer parameter sharing which are two methods of parameter reduction. They also introduced a new loss function and replaced it with one of the loss functions being used in BERT (i.e. NSP). The last change is removing dropout from the model.