通过高质量难负样本提升多语言稠密检索的数据利用效率
Boosting Data Utilization for Multilingual Dense Retrieval
- 构建难负样本生成机制，提升负样本质量
- 在16语言的MIRACL数据集上超越多个强基线
- 适合关注多语言信息检索与数据高效利用的研究者
多语言稠密检索旨在基于统一的检索模型跨语言获取相关文档，其核心挑战在于将不同语言的表示对齐到共享向量空间。现有方法通常通过对比学习微调稠密检索器，但性能高度依赖负样本质量与小批量数据的有效性。不同于以往聚焦复杂模型架构的研究，本文提出一种提升多语言稠密检索中数据利用率的方法，通过生成高质量难负样本并优化小批量数据构成。在包含16种语言的多语言检索基准MIRACL上的大量实验表明，该方法显著优于多个现有强基线。
原文摘要 · Abstract (English)
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.
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