  Multilingual Pretrained Language Models (MPLMs) have shown their strong
multilinguality in recent empirical cross-lingual transfer studies. In this
paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC)
pipeline to improve the zero-shot performance on low-resource languages (LRLs)
by augmenting the context with semantically similar sentences retrieved from a
high-resource language (HRL) as prompts. PARC improves the zero-shot
performance on three downstream tasks (binary sentiment classification, topic
categorization and natural language inference) with multilingual parallel test
sets across 10 LRLs covering 6 language families in both unlabeled settings
(+5.1%) and labeled settings (+16.3%). PARC-labeled also outperforms the
finetuning baseline by 3.7%. We find a significant positive correlation between
cross-lingual transfer performance on one side, and the similarity between the
high- and low-resource languages as well as the amount of low-resource
pretraining data on the other side. A robustness analysis suggests that PARC
has the potential to achieve even stronger performance with more powerful
MPLMs.
