This week, Nishant will give us a review on a paper about multilingual BERT. A zoom link will be sent tomorrow morning.
How multilingual is multilingual BERT?
Abstract: This work by Pires et al. (2019) empirically investigates the degree to which pre-trained contextualized general-purpose linguistic representations generalize across languages. The key finding is 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, authors 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.
Tuesday, Apr 20th, 09:30 a.m.
