titleSemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges
authorsSeongtae Hong, Youngjoon Jang, Jia-Heui Ju, Hyeonseok Moon, Heuiseok Lim
date2026-05-25
venuearXiv (preprint, not peer reviewed)
idarXiv:2605.26002
affiliationNLP & AI - Korea University
upvotes5 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractSparse encoders offer high-precision retrieval by representing term importance within a vocabulary space, yet their English-centric structures pose a critical impediment to language transfer for non-English languages. To overcome this structural limitation, we propose SemBridge, a novel embedding initialization method designed for cross-lingual adaptation in sparse encoders by leveraging multilingual bridge models. SemBridge establishes semantic alignments between source and target vocabularies using multilingual dense embeddings as a bridge. Rather than directly relying on all source tokens, SemBridge selects a small set of semantically related source-language tokens and uses them to initialize each target-language token, effectively filtering out semantic noise and reconstructing target tokens as precise linear combinations of core synonyms. This accelerates convergence during fine-tuning and improves training efficiency. Extensive experiments across five languages and four sparse architectures demonstrate that SemBridge achieves superior zero-shot retrieval performance and consistently improves retrieval performance after fine-tuning compared to existing baselines. These results validate SemBridge as a practical solution for deploying high-performance sparse retrieval systems in diverse linguistic environments.

cite

@misc{hong2026sembridge,
  title={SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges},
  author={Seongtae Hong and Youngjoon Jang and Jia-Heui Ju and Hyeonseok Moon and Heuiseok Lim},
  year={2026},
  eprint={2605.26002},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2605.26002}
}