Nearest Neighbour Few-Shot Learning for Cross-lingual Classification
M Saiful Bari, Batool Haider, Saab Mansour · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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.* Work done while Saiful was interning at Amazon AI 1 We loosely use the term LM to describe unsupervised pretrained models including Masked-LMs and Causal-LMs