Boosting Data Utilization for Multilingual Dense Retrieval

2025-09-11

AI Summary

The core challenge in multilingual dense retrieval lies in cross-lingual semantic alignment, yet existing approaches predominantly rely on complex model architectures while overlooking the critical role of training data quality in representation learning. This paper proposes a data-driven optimization paradigm that focuses on improving the quality of hard negative mining and the efficiency of mini-batch construction. Specifically, we enhance the contrastive learning framework with a language-aware hard negative mining strategy and introduce lightweight cross-lingual data augmentation. Crucially, our method requires no architectural modifications to the underlying retriever. Evaluated on the 16-language MIRACL benchmark, it significantly outperforms multiple strong baselines. Results demonstrate that high-quality training data—not just model capacity—plays a decisive role in achieving effective multilingual representation alignment. Our work provides a novel, efficient, and scalable pathway for multilingual dense retrieval, emphasizing data-centric optimization over model-centric complexity.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model architecture, we propose a method to boost data utilization for multilingual dense retrieval by obtaining high-quality hard negative samples and effective mini-batch data. The extensive experimental results on a multilingual retrieval benchmark, MIRACL, with 16 languages demonstrate the effectiveness of our method by outperforming several existing strong baselines.

Problem

• Aligning multilingual representations in shared vector space
• Improving negative sample quality for contrastive learning
• Enhancing mini-batch data efficacy in dense retrieval

Innovation

• High-quality hard negative samples generation
• Effective mini-batch data utilization
• Multilingual dense retrieval enhancement