  A reasonable amount of annotated data is required for fine-tuning pre-trained
language models (PLM) on downstream tasks. However, obtaining labeled examples
for different language varieties can be costly. In this paper, we investigate
the zero-shot performance on Dialectal Arabic (DA) when fine-tuning a PLM on
modern standard Arabic (MSA) data only -- identifying a significant performance
drop when evaluating such models on DA. To remedy such performance drop, we
propose self-training with unlabeled DA data and apply it in the context of
named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm
detection (SRD) on several DA varieties. Our results demonstrate the
effectiveness of self-training with unlabeled DA data: improving zero-shot
MSA-to-DA transfer by as large as $\sim$10\% F$_1$ (NER), 2\% accuracy (POS
tagging), and 4.5\% F$_1$ (SRD). We conduct an ablation experiment and show
that the performance boost observed directly results from the unlabeled DA
examples used for self-training. Our work opens up opportunities for leveraging
the relatively abundant labeled MSA datasets to develop DA models for zero and
low-resource dialects. We also report new state-of-the-art performance on all
three tasks and open-source our fine-tuned models for the research community.
