Naver Labs Europe @ WSDM CUP | Multilingual Retrieval
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914347360452608 |
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| author | Formal, Thibault Louis, Maxime Déjean, Hervé Clinchant, Stéphane |
| author_facet | Formal, Thibault Louis, Maxime Déjean, Hervé Clinchant, Stéphane |
| contents | This report presents our participation to the WSDM Cup 2026 shared task on multilingual document retrieval from English queries. The task provides a challenging benchmark for cross-lingual generalization. It also provides a natural testbed for evaluating SPLARE, our recently proposed learned sparse retrieval model, which produces generalizable sparse latent representations and is particularly well suited to multilingual retrieval settings.
We evaluate five progressively enhanced runs, starting from a SPLARE-7B model and incorporating lightweight improvements, including reranking with Qwen3-Reranker-4B and simple score fusion strategies. Our results demonstrate the strength of SPLARE compared to state-of-the-art dense baselines such as Qwen3-8B-Embed. More broadly, our submission highlights the continued relevance and competitiveness of learned sparse retrieval models beyond English-centric scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20986 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Naver Labs Europe @ WSDM CUP | Multilingual Retrieval Formal, Thibault Louis, Maxime Déjean, Hervé Clinchant, Stéphane Information Retrieval This report presents our participation to the WSDM Cup 2026 shared task on multilingual document retrieval from English queries. The task provides a challenging benchmark for cross-lingual generalization. It also provides a natural testbed for evaluating SPLARE, our recently proposed learned sparse retrieval model, which produces generalizable sparse latent representations and is particularly well suited to multilingual retrieval settings. We evaluate five progressively enhanced runs, starting from a SPLARE-7B model and incorporating lightweight improvements, including reranking with Qwen3-Reranker-4B and simple score fusion strategies. Our results demonstrate the strength of SPLARE compared to state-of-the-art dense baselines such as Qwen3-8B-Embed. More broadly, our submission highlights the continued relevance and competitiveness of learned sparse retrieval models beyond English-centric scenarios. |
| title | Naver Labs Europe @ WSDM CUP | Multilingual Retrieval |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2602.20986 |