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    <abstract>Sparse 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.</abstract>
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%0 Conference Proceedings
%T SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridge
%A Hong, Seongtae
%A Jang, Youngjoon
%A Ju, Jia-Huei
%A Moon, Hyeonseok
%A Lim, Heuiseok
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hong-etal-2026-sembridge
%X Sparse 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.
%U https://aclanthology.org/2026.emnlp-main.622/
%P 14291-14305

Markdown (Informal)

[SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridge](https://aclanthology.org/2026.emnlp-main.622/) (Hong et al., EMNLP 2026)

• SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridge (Hong et al., EMNLP 2026)

ACL

• Seongtae Hong, Youngjoon Jang, Jia-Huei Ju, Hyeonseok Moon, and Heuiseok Lim. 2026. SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridge. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14291–14305, Budapest, Hungary. Association for Computational Linguistics.