Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval
Multilingual dense retrieval aims to handle queries and documents across different languages based on a unified retriever model. The challenge lies in enabling robust retrieval transfer to low-resource languages where annotated retrieval data is often scarce. Although previous studies transfer high-resource supervision to low-resource languages in multilingual semantic representation learning, the shared representation often entangles semantic and linguistic features, which may interfere with optimizing semantic relevance for retrieval. Different from existing methods that focus on learning language-agnostic semantic features under such entanglement, we propose a disentangled contrastive ...
| Authors | Chao Huang, Yufeng Chen, Changhao Guan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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