  Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have
led to significant performance gains on a wide range of cross-lingual NLP
tasks, success on many downstream tasks still relies on the availability of
sufficient annotated data. Traditional fine-tuning of pre-trained models using
only a few target samples can cause over-fitting. This can be quite limiting as
most languages in the world are under-resourced. In this work, we investigate
cross-lingual adaptation using a simple nearest neighbor few-shot (<15 samples)
inference technique for classification tasks. We experiment using a total of 16
distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach
consistently improves traditional fine-tuning using only a handful of labeled
samples in target locales. We also demonstrate its generalization capability
across tasks.
