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        <title>Nearest Neighbour Few-Shot Learning for Cross-lingual Classification</title>
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        <namePart type="given">Saiful</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Lucia</namePart>
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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 (&lt;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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.131</identifier>
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%0 Conference Proceedings
%T Nearest Neighbour Few-Shot Learning for Cross-lingual Classification
%A Bari, M. Saiful
%A Haider, Batool
%A Mansour, Saab
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F bari-etal-2021-nearest
%X 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.
%R 10.18653/v1/2021.emnlp-main.131
%U https://aclanthology.org/2021.emnlp-main.131
%U https://doi.org/10.18653/v1/2021.emnlp-main.131
%P 1745-1753

Markdown (Informal)

[Nearest Neighbour Few-Shot Learning for Cross-lingual Classification](https://aclanthology.org/2021.emnlp-main.131) (Bari et al., EMNLP 2021)

• Nearest Neighbour Few-Shot Learning for Cross-lingual Classification (Bari et al., EMNLP 2021)

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.