Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

M Saiful Bari; Batool Haider; Saab Mansour

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arXiv · 2109.02221

Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

M Saiful Bari·Batool Haider·Saab Mansour

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Abstract

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.

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M Saiful Bari, Batool Haider, Saab Mansour. 2021-09-06. Nearest Neighbour Few-Shot Learning for Cross-lingual Classification. https://arxiv.org/abs/2109.02221

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