SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges

SemBridge is a new cross-lingual adaptation method for sparse retrieval models.

It maps target-language tokens to semantically aligned source-token combinations using multilingual dense embeddings.

This improves training convergence, efficiency, and retrieval quality across five languages.

The approach helps sparse retrieval systems transfer beyond English more effectively.