Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Fei Wang Kuan-Hao Huang Kai-Wei Chang Muhao Chen
Zero-shot cross-lingual transfer is a central task in multilingual NLP,
allowing models trained in languages with more sufficient training resources to
generalize to other low-resource languages. Earlier efforts on this task use
parallel corpora, bilingual dictionaries, or other annotated alignment data to
improve cross-lingual transferability, which are typically expensive to obtain.
In this paper, we propose a simple yet effective method, SALT, to improve the
zero-shot cross-lingual transfer of the multilingual pretrained language models
without the help of such external data. By incorporating code-switching and
embedding mixup with self-augmentation, SALT effectively distills cross-lingual
knowledge from the multilingual PLM and enhances its transferability on
downstream tasks. Experimental results on XNLI and PAWS-X show that our method
is able to improve zero-shot cross-lingual transferability without external
data. Our code is available at https://github.com/luka-group/SALT.