Naver Labs Europe @ WSDM CUP | Multilingual Retrieval

TL;DR

This paper describes the participation of Naver Labs Europe in the WSDM CUP 2026, evaluating the SPLARE sparse retrieval model for multilingual document retrieval from English queries, showing its competitive performance.

Contribution

The paper introduces the application and evaluation of the SPLARE learned sparse retrieval model in a multilingual setting, demonstrating its effectiveness over dense retrieval baselines.

Findings

SPLARE outperforms dense baselines like Qwen3-8B-Embed.

Lightweight improvements enhance retrieval performance.

Learned sparse models remain competitive in multilingual retrieval.

Abstract

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.