调整训练数据语言组合，提升跨语言检索性能。
Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging
- 构建韩英平行语料，测试不同语言组合对检索影响。
- 特定语言配对提升跨语言检索，但会降低单语检索性能。
- 模型融合可平衡两者，适合多语言应用开发人员。
随着多语言文本信息的广泛应用，跨语言信息检索（CLIR）成为关键研究方向。然而，训练数据的语言构成对CLIR和单语信息检索（IR）性能的影响仍缺乏系统研究。为此，我们构建了语言对齐的韩英平行数据集，并采用不同语言组合训练检索模型。实验表明，训练数据的语言构成显著影响检索性能，存在重要跨语言关联：特定语言对能提升CLIR效果，但会损害单语IR表现。我们的研究证明，模型融合能有效缓解这一权衡，在保持单语检索能力的同时实现优异的跨语言检索结果。研究揭示了训练数据语言配置对两类任务的深远影响，并提出模型融合是一种优化多任务性能的有效策略。
原文摘要 · Abstract (English)
With the increasing utilization of multilingual text information, Cross-Lingual Information Retrieval (CLIR) has become a crucial research area. However, the impact of training data composition on both CLIR and Mono-Lingual Information Retrieval (IR) performance remains under-explored. To systematically investigate this data-centric aspect, we construct linguistically parallel Korean-English datasets and train retrieval models with various language combinations. Our experiments reveal that the language composition of training data significantly influences IR performance, exhibiting important inter-lingual correlations: CLIR performance improves with specific language pairs, while Mono-Lingual IR performance declines. Our work demonstrates that Model Merging can effectively mitigate this trade-off, achieving strong CLIR results while preserving Mono-Lingual IR capabilities. Our findings underscore the effects of linguistic configuration of training data on both CLIR and Mono-Lingual IR, and present Model Merging as a viable strategy to optimize performance across these tasks.
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