InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training

Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou


Abstract
In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the existing methods for learning cross-lingual representations. More importantly, inspired by the framework, we propose a new pre-training task based on contrastive learning. Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples. By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models. Experimental results on several benchmarks show that our approach achieves considerably better performance. The code and pre-trained models are available at https://aka.ms/infoxlm.
Anthology ID:
2021.naacl-main.280
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3576–3588
Language:
URL:
https://aclanthology.org/2021.naacl-main.280
DOI:
10.18653/v1/2021.naacl-main.280
Bibkey:
Cite (ACL):
Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, and Ming Zhou. 2021. InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3576–3588, Online. Association for Computational Linguistics.
Cite (Informal):
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (Chi et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.280.pdf
Video:
 https://aclanthology.org/2021.naacl-main.280.mp4
Code
 additional community code
Data
CC100MLQAXNLIXTREME