Synthetic Query Augmentation for Zero-Shot Cross-Lingual Dense Retrieval in Low-Resource Languages

Abstract

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 la

Research Question

To what extent does synthetic query augmentation improve zero-shot cross-lingual dense retrieval performance on low-resource language pairs within the XNLI benchmark compared to high-resource pairs?

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

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