Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

M Saiful Bari, Batool Haider, Saab Mansour


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.
Anthology ID:
2021.emnlp-main.131
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1745–1753
Language:
URL:
https://aclanthology.org/2021.emnlp-main.131
DOI:
10.18653/v1/2021.emnlp-main.131
Bibkey:
Cite (ACL):
M Saiful Bari, Batool Haider, and Saab Mansour. 2021. Nearest Neighbour Few-Shot Learning for Cross-lingual Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1745–1753, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Nearest Neighbour Few-Shot Learning for Cross-lingual Classification (Bari et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.131.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.131.mp4
Data
XNLI