A major challenge in cross-language information retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects term weighting which is known to highly impact the retrieval performance. Despite its importance, how different approaches for integration of translation knowledge into retrieval models relatively perform has not been analytically examined. In this paper, we present an analytical investigation of using translation knowledge in CLIR. In particular, by adopting the axiomatic analysis framework, we formulate impacts of using translation knowledge on document ranking as constraints that any cross-language retrieval model should satisfy. We then consider state-of-the-art CLIR methods and check whether they satisfy these constraints. Our study shows that none of the existing methods satisfies all constraints. Based on the defined constraints, we propose the hierarchical query modeling method for CLIR which satisfies more constraints and achieves a higher CLIR performance, compared to the existing methods.

中文翻译：

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基于语料库的跨语言信息检索的公理方法

跨语言信息检索（CLIR）的主要挑战是在检索模型中采用翻译知识，因为它会影响术语权重，众所周知，术语权重会极大地影响检索性能。尽管很重要，但尚未分析分析将翻译知识整合到检索模型中的不同方法的相对性能。在本文中，我们对在CLIR中使用翻译知识进行了分析研究。特别是，通过采用公理化分析框架，我们将使用翻译知识对文档排名的影响表述为任何跨语言检索模型都应满足的约束条件。然后，我们考虑最先进的CLIR方法，并检查它们是否满足这些约束。我们的研究表明，现有方法均不能满足所有约束条件。