Emerging Cross-lingual Structure in Pretrained Language Models

Alexis Conneau, Shijie Wu, Haoran Li, Luke Zettlemoyer, Veselin Stoyanov


Abstract
We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from monolingual BERT models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries are automatically discovered and aligned during the joint training process.
Anthology ID:
2020.acl-main.536
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6022–6034
Language:
URL:
https://aclanthology.org/2020.acl-main.536
DOI:
10.18653/v1/2020.acl-main.536
Bibkey:
Cite (ACL):
Alexis Conneau, Shijie Wu, Haoran Li, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Emerging Cross-lingual Structure in Pretrained Language Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6022–6034, Online. Association for Computational Linguistics.
Cite (Informal):
Emerging Cross-lingual Structure in Pretrained Language Models (Conneau et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.536.pdf
Video:
 http://slideslive.com/38928831
Data
XNLI