With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the language of documents differs from that of queries, and typically, the documents are composed in a single coherent language. In this paper, we highlight that in such a setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in a document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross-lingual performance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only a small dataset consisting of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Extensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.

翻译：随着多语言文档的可访问性和利用率不断提高，跨语言信息检索（CLIR）已成为一个重要的研究领域。传统上，CLIR任务是在文档语言与查询语言不同的设置下进行的，且文档通常由一种连贯的语言撰写。本文指出，在这种设置下，跨语言对齐能力可能无法得到充分评估。具体而言，我们观察到，在英文文档与另一种语言共存的文档池中，大多数多语言检索器倾向于优先选择不相关的英文文档，而非与查询语言相同的相关文档。为了严格分析和量化这一现象，我们引入了多种场景和指标，用于评估多语言检索模型的跨语言对齐性能。此外，为在这些具有挑战性的条件下提升跨语言性能，我们提出了一种新颖的训练策略，旨在增强跨语言对齐。仅使用包含2.8k样本的小型数据集，我们的方法显著提升了跨语言检索性能，同时缓解了英文倾向问题。广泛的分析表明，所提出的方法有效增强了大多数多语言嵌入模型的跨语言对齐能力。