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    <abstract>Prompting shows promising results in few-shot scenarios. However, its strength for multilingual/cross-lingual problems has not been fully exploited. hao and Schütze (2021) made initial explorations in this direction by presenting that cross-lingual prompting outperforms cross-lingual finetuning. In this paper, we conduct an empirical exploration on the effect of each component in cross-lingual prompting and derive Universal Prompting, which helps alleviate the discrepancies between source-language training and target-language inference. Based on this, we propose DPA, a dual prompt augmentation framework, aiming at relieving the data scarcity issue in few-shot cross-lingual prompting. Notably, for XNLI, our method achieves 46.54% with only 16 English training examples per class, significantly better than 34.99% of fine-tuning. Our code is available at https://github.com/DAMO-NLP-SG/DPA.</abstract>
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%0 Conference Proceedings
%T Enhancing Cross-lingual Prompting with Dual Prompt Augmentation
%A Zhou, Meng
%A Li, Xin
%A Jiang, Yue
%A Bing, Lidong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-enhancing
%X Prompting shows promising results in few-shot scenarios. However, its strength for multilingual/cross-lingual problems has not been fully exploited. hao and Schütze (2021) made initial explorations in this direction by presenting that cross-lingual prompting outperforms cross-lingual finetuning. In this paper, we conduct an empirical exploration on the effect of each component in cross-lingual prompting and derive Universal Prompting, which helps alleviate the discrepancies between source-language training and target-language inference. Based on this, we propose DPA, a dual prompt augmentation framework, aiming at relieving the data scarcity issue in few-shot cross-lingual prompting. Notably, for XNLI, our method achieves 46.54% with only 16 English training examples per class, significantly better than 34.99% of fine-tuning. Our code is available at https://github.com/DAMO-NLP-SG/DPA.
%R 10.18653/v1/2023.findings-acl.700
%U https://aclanthology.org/2023.findings-acl.700
%U https://doi.org/10.18653/v1/2023.findings-acl.700
%P 11008-11020

Markdown (Informal)

[Enhancing Cross-lingual Prompting with Dual Prompt Augmentation](https://aclanthology.org/2023.findings-acl.700) (Zhou et al., Findings 2023)

• Enhancing Cross-lingual Prompting with Dual Prompt Augmentation (Zhou et al., Findings 2023)

ACL

• Meng Zhou, Xin Li, Yue Jiang, and Lidong Bing. 2023. Enhancing Cross-lingual Prompting with Dual Prompt Augmentation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11008–11020, Toronto, Canada. Association for Computational Linguistics.