Gene

Linguistics

Philosophy

Machine translation

Scripting language

Messenger RNA

Zero (linguistics)

Economics

Set (abstract data type)

Multilingualism

Biochemistry

Parallel computing

Computer science

Code (set theory)

Translation (biology)

Language model

Natural language processing

Transfer (computing)

Task (project management)

Management

Artificial intelligence

Chemistry

Programming language

In this paper, we show that Multilingual BERT (M-BERT), released by Devlin et al. (2018) as a single language model pre-trained from monolingual corpora in 104 languages, is surprisingly good at zero-shot cross-lingual model transfer, in which task-specific annotations in one language are used to fine-tune the model for evaluation in another language. To understand why, we present a large number of probing experiments, showing that transfer is possible even to languages in different scripts, that transfer works best between typologically similar languages, that monolingual corpora can train models for code-switching, and that the model can find translation pairs. From these results, we can conclude that M-BERT does create multilingual representations, but that these representations exhibit systematic deficiencies affecting certain language pairs.

How Multilingual is Multilingual BERT?