什么推动跨语言排序？具有多语言语言模型的检索方法
What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Models
AI总结：
本文研究了跨语言检索中影响排序的因素，发现专门训练的密集检索模型和对比学习在低资源和跨脚本场景中表现更优，优于传统的翻译和单语言检索方法。
摘要：
跨语言信息检索（CLIR）使用户能够访问多语言知识，但仍然具有挑战性，因为资源、脚本和嵌入模型中的跨语言语义对齐薄弱。现有的流程往往依赖于翻译和单语言检索启发法，这会增加计算开销和噪声，从而降低性能。本文系统评估了四种干预类型，即文档翻译、预训练编码器的多语言密集检索、词、短语和查询-文档级别的对比学习，以及交叉编码重新排序，跨三个基准数据集。我们发现，专门针对CLIR训练的密集检索模型在性能上始终优于词法匹配方法，并且从文档翻译中获得很少的收益。对比学习减轻了语言偏见，并为初始对齐薄弱的编码器带来显著改进，而重新排序可以有效，但依赖于交叉编码训练数据的质量。尽管高资源语言仍然主导整体性能，但相对于词法和文档翻译基线的提升最显著的是低资源和跨脚本对。这些发现表明，跨语言搜索系统应优先考虑语义多语言嵌入和基于目标学习的对齐，而不是基于翻译的流程，特别是对于跨脚本和欠资源语言。
英文摘要：
Cross-lingual information retrieval (CLIR) enables access to multilingual knowledge but remains challenging due to disparities in resources, scripts, and weak cross-lingual semantic alignment in embedding models. Existing pipelines often rely on translation and monolingual retrieval heuristics, which add computational overhead and noise, degrading performance. This work systematically evaluates four intervention types, namely document translation, multilingual dense retrieval with pretrained encoders, contrastive learning at word, phrase, and query-document levels, and cross-encoder re-ranking, across three benchmark datasets. We find that dense retrieval models trained specifically for CLIR consistently outperform lexical matching methods and derive little benefit from document translation. Contrastive learning mitigates language biases and yields substantial improvements for encoders with weak initial alignment, and re-ranking can be effective, but depends on the quality of the cross-encoder training data. Although high-resource languages still dominate overall performance, gains over lexical and document-translated baselines are most pronounced for low-resource and cross-script pairs. These findings indicate that cross-lingual search systems should prioritise semantic multilingual embeddings and targeted learning-based alignment over translation-based pipelines, particularly for cross-script and under-resourced languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
