Milco: Learned Sparse Retrieval Across Languages via a Multilingual Connector

Learned Sparse Retrieval (LSR) combines the efficiency of bi-encoders with the transparency of lexical matching, but existing approaches struggle to scale beyond English. We introduce MILCO, an LSR architecture that maps queries and documents from different languages into a shared English lexical space via a multilingual connector. MILCO is trained with a specialized two-stage regime that combines Sparse Alignment Pretraining with contrastive training to provide representation transparency and effectiveness while mitigating semantic collapse. MILCO achieves state-of-the-art multilingual and cross-lingual LSR performance, outperforming leading dense, sparse, and multi-vector baselines such as BGE-M3 and Qwen3-Embed on standard multilingual benchmarks, while supporting dynamic efficiency through post-hoc pruning. Notably, when using mass-based pruning to reduce document representations to only 30 active dimensions on average, MILCO 560M outperforms the similarly-sized Qwen3-Embed 0.6B with 1024 dimensions. Learned Sparse Retrieval (LSR)(MacAvaney et al., 2020; Formal et al., 2021; Nguyen et al., 2023) represents queries and documents as sparse lexical embeddings and retains the scalability benefits of bi-encoders. Unlike dense methods, LSR aligns representation with a natural language vocabulary, yielding transparent representations that facilitate error tracing and bias inspection. LSR naturally supports dynamic post-hoc pruning at inference time (Bruch et al., 2024), providing Matryoshka-like latency control (Kusupati et al., 2022) without requiring auxiliary training objectives. Empirically, LSR (Lassance et al., 2024; Lei et al., 2025) is competitive on benchmarks like BEIR (Thakur et al., 2021) and MTEB (Enevoldsen et al., 2025).