| Autor: |
Bari, M Saiful, Haider, Batool, Mansour, Saab |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
The 2021 Conference on Empirical Methods in Natural Language Processing |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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