Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the language of documents differs from that of queries, and typically, the documents are composed in a single coherent language. In this paper, we highlight that in such a setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in a document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross-lingual performance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only a small dataset consisting of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Extensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.
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Seongtae Hong
claimed_verified
2026-04-08T16:33:58.927Z
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Seongtae Hong
hongst
user
hongst
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Youngjoon Jang
claimed_verified
2026-04-09T18:50:52.638Z
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Youngjoon Jang
yjoonjang
user
yjoonjang
69d65b9bc6ed9968ac5c6717
Jungseob Lee
69d65b9bc6ed9968ac5c6718
Hyeonseok Moon
69d65b9bc6ed9968ac5c6719
Heuiseok Lim
Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the language of documents differs from that of queries, and typically, the documents are composed in a single coherent language. In this paper, we highlight that in such a setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in a document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross-lingual performance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only a small dataset consisting of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Extensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.
Multilingual retrieval models exhibit bias toward English documents in mixed-language document pools, which is addressed through a novel training strategy that improves cross-lingual alignment with minimal data.
cross-lingual information retrieval multilingual retrieval models cross-lingual alignment multilingual embedding models