Improving Arabic Information Retrieval and Reranking Performance using Knowledge Distillation

What is it about?

This work presents a cross-lingual knowledge distillation approach designed to improve Arabic information retrieval and reranking performance by transferring relevance knowledge from high-resource English IR models. Leveraging the richer supervision available in English, the method uses teacher–student training to generate soft relevance signals that guide Arabic bi-encoder and cross-encoder models, enabling them to learn effective ranking behavior despite limited native data. Evaluated on the mMARCO Arabic passage-ranking benchmark, the distilled models achieve significant gains over existing multilingual baselines. The study demonstrates that retrieval expertise can be successfully transferred across languages, offering a scalable solution for building strong IR systems in low-resource settings.

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Photo by Jona on Unsplash

Photo by Jona on Unsplash

Why is it important?

This work is important because it addresses a fundamental limitation in Arabic information retrieval: the lack of large, high-quality relevance-labeled datasets needed to train modern neural ranking models. By transferring ranking expertise from well-established English IR models through cross-lingual knowledge distillation, the proposed approach enables Arabic retrievers to benefit from resources and training signals that do not exist in the Arabic ecosystem. This method offers a scalable and cost-efficient alternative to human annotation, significantly improves state-of-the-art performance on Arabic retrieval benchmarks, and demonstrates that relevance modeling can be effectively transferred across languages. More broadly, it contributes to reducing the disparity between high-resource and low-resource languages in IR, supporting greater linguistic inclusivity in search and NLP technologies.

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