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    <abstract>Multilingual Pretrained Language Models (MPLMs) perform strongly in cross-lingual transfer. We propose Prompts Augmented by Retrieval Crosslingually (PARC) to improve zero-shot performance on low-resource languages (LRLs) by augmenting the context with prompts consisting of semantically similar sentences retrieved from a high-resource language (HRL). PARC improves zero-shot performance on three downstream tasks (sentiment classification, topic categorization, natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled (+5.1%) and labeled settings (+16.3%). PARC also outperforms finetuning by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages
%A Nie, Ercong
%A Liang, Sheng
%A Schmid, Helmut
%A Schütze, Hinrich
%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 nie-etal-2023-cross
%X Multilingual Pretrained Language Models (MPLMs) perform strongly in cross-lingual transfer. We propose Prompts Augmented by Retrieval Crosslingually (PARC) to improve zero-shot performance on low-resource languages (LRLs) by augmenting the context with prompts consisting of semantically similar sentences retrieved from a high-resource language (HRL). PARC improves zero-shot performance on three downstream tasks (sentiment classification, topic categorization, natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled (+5.1%) and labeled settings (+16.3%). PARC also outperforms finetuning by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.
%R 10.18653/v1/2023.findings-acl.528
%U https://aclanthology.org/2023.findings-acl.528
%U https://doi.org/10.18653/v1/2023.findings-acl.528
%P 8320-8340

Markdown (Informal)

[Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages](https://aclanthology.org/2023.findings-acl.528) (Nie et al., Findings 2023)

• Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages (Nie et al., Findings 2023)

ACL

• Ercong Nie, Sheng Liang, Helmut Schmid, and Hinrich Schütze. 2023. Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8320–8340, Toronto, Canada. Association for Computational Linguistics.