XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei


Abstract
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora. Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.
Anthology ID:
2022.acl-long.427
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6170–6182
Language:
URL:
https://aclanthology.org/2022.acl-long.427
DOI:
10.18653/v1/2022.acl-long.427
Bibkey:
Cite (ACL):
Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, and Furu Wei. 2022. XLM-E: Cross-lingual Language Model Pre-training via ELECTRA. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6170–6182, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (Chi et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.427.pdf
Code
 microsoft/unilm +  additional community code
Data
CC100MLQAPAWS-XTyDi QAXNLIXQuADXTREME