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        <title>MIMO: Multilingual Information Retrieval via Monolingual Objectives</title>
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    <abstract>Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different languages within a mixed-language corpus. However, existing embedding models are primarily optimized for Multi-Monolingual retrieval and their performance often degrades substantially in MLIR settings. Moreover, directly applying conventional contrastive learning to MLIR can exacerbate language clustering and expose a trade-off between cross-lingual alignment and embedding uniformity. To address these limitations, we propose MIMO: Multilingual Information Retrieval via Monolingual Objectives, a two-stage framework that uses a stable English semantic space as an anchor. MIMO first initializes cross-lingual alignment through knowledge distillation, and then jointly optimizes distillation and cross-lingual contrastive learning to improve retrieval discrimination while preserving alignment. Extensive experiments show that MIMO consistently outperforms existing cross-lingual training baselines across various MLIR and Multi-Monolingual benchmarks. MIMO also remains competitive with off-the-shelf models of similar or larger parameter scales. Furthermore, our cross-lingual Alignment-Uniformity analysis clarifies the distinct roles of the two loss components and shows that their combination yields a favorable trade-off between alignment and uniformity.</abstract>
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%0 Conference Proceedings
%T MIMO: Multilingual Information Retrieval via Monolingual Objectives
%A Jang, Youngjoon
%A Hong, Seongtae
%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 jang-etal-2026-mimo
%X Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different languages within a mixed-language corpus. However, existing embedding models are primarily optimized for Multi-Monolingual retrieval and their performance often degrades substantially in MLIR settings. Moreover, directly applying conventional contrastive learning to MLIR can exacerbate language clustering and expose a trade-off between cross-lingual alignment and embedding uniformity. To address these limitations, we propose MIMO: Multilingual Information Retrieval via Monolingual Objectives, a two-stage framework that uses a stable English semantic space as an anchor. MIMO first initializes cross-lingual alignment through knowledge distillation, and then jointly optimizes distillation and cross-lingual contrastive learning to improve retrieval discrimination while preserving alignment. Extensive experiments show that MIMO consistently outperforms existing cross-lingual training baselines across various MLIR and Multi-Monolingual benchmarks. MIMO also remains competitive with off-the-shelf models of similar or larger parameter scales. Furthermore, our cross-lingual Alignment-Uniformity analysis clarifies the distinct roles of the two loss components and shows that their combination yields a favorable trade-off between alignment and uniformity.
%U https://aclanthology.org/2026.emnlp-main.156/
%P 3597-3612

Markdown (Informal)

[MIMO: Multilingual Information Retrieval via Monolingual Objectives](https://aclanthology.org/2026.emnlp-main.156/) (Jang et al., EMNLP 2026)

• MIMO: Multilingual Information Retrieval via Monolingual Objectives (Jang et al., EMNLP 2026)

ACL

• Youngjoon Jang, Seongtae Hong, and Heuiseok Lim. 2026. MIMO: Multilingual Information Retrieval via Monolingual Objectives. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 3597–3612, Budapest, Hungary. Association for Computational Linguistics.