Larger-Scale Transformers for Multilingual Masked Language Modeling
- URL: http://arxiv.org/abs/2105.00572v1
- Date: Sun, 2 May 2021 23:15:02 GMT
- Title: Larger-Scale Transformers for Multilingual Masked Language Modeling
- Authors: Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau
- Abstract summary: Two new models dubbed XLM-R XL and XLM-R XXL outperform XLM-R by 1.8% and 2.4% average accuracy on XNLI.
Our model also outperforms the RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on average while handling 99 more languages.
- Score: 16.592883204398518
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent work has demonstrated the effectiveness of cross-lingual language
model pretraining for cross-lingual understanding. In this study, we present
the results of two larger multilingual masked language models, with 3.5B and
10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform
XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the
RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on
average while handling 99 more languages. This suggests pretrained models with
larger capacity may obtain both strong performance on high-resource languages
while greatly improving low-resource languages. We make our code and models
publicly available.
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