When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer

Ameet Deshpande, Partha Talukdar, Karthik Narasimhan


Abstract
While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer. Analyses involving pairs of natural languages are often inconclusive and contradictory since languages simultaneously differ in many linguistic aspects. In this paper, we perform a large-scale empirical study to isolate the effects of various linguistic properties by measuring zero-shot transfer between four diverse natural languages and their counterparts constructed by modifying aspects such as the script, word order, and syntax. Among other things, our experiments show that the absence of sub-word overlap significantly affects zero-shot transfer when languages differ in their word order, and there is a strong correlation between transfer performance and word embedding alignment between languages (e.g., 𝜌s=0.94 on the task of NLI). Our results call for focus in multilingual models on explicitly improving word embedding alignment between languages rather than relying on its implicit emergence.
Anthology ID:
2022.naacl-main.264
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3610–3623
Language:
URL:
https://aclanthology.org/2022.naacl-main.264
DOI:
10.18653/v1/2022.naacl-main.264
Bibkey:
Cite (ACL):
Ameet Deshpande, Partha Talukdar, and Karthik Narasimhan. 2022. When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3610–3623, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer (Deshpande et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.264.pdf
Code
 princeton-nlp/MultilingualAnalysis
Data
XQuAD