Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure

Yuan Chai, Yaobo Liang, Nan Duan


Abstract
Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data. Despite the encouraging results, we still lack a clear understanding of why cross-lingual ability could emerge from multilingual MLM. In our work, we argue that cross-language ability comes from the commonality between languages. Specifically, we study three language properties: constituent order, composition and word co-occurrence. First, we create an artificial language by modifying property in source language. Then we study the contribution of modified property through the change of cross-language transfer results on target language. We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval). Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
Anthology ID:
2022.acl-long.322
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4702–4712
Language:
URL:
https://aclanthology.org/2022.acl-long.322
DOI:
10.18653/v1/2022.acl-long.322
Bibkey:
Cite (ACL):
Yuan Chai, Yaobo Liang, and Nan Duan. 2022. Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4702–4712, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure (Chai et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.322.pdf
Data
XNLI