跨语言事实核查检索比单语更难，但大模型重排和相似句负样本能显著提升效果。
Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches
- 用句子相似度选负样本，提升监督训练效果
- 大模型重排使跨语言检索准确率最高
- 跨语言任务特性独立于单语，需专门设计
事实核查声明的检索是一项成熟任务，自动化可辅助专业核查员进行信息验证。以往工作多局限于单语场景（输入与检索声明同语言），但在事实核查资源稀缺的语言或全球性议题（如疫情、战争、国际政治）中，跨语言检索至关重要。本文研究提升多语言与跨语言检索性能的策略：在监督设置中优化负例选择，在无监督设置中采用重排机制。我们在包含47种语言（283种语言组合）的公开数据集上评估所有方法，结果表明基于大模型的重排表现最佳，其次为采用句子相似度采样负例的微调策略。最重要的是，我们发现跨语言设置具有区别于多语言设置的独特特性，需针对性优化。
原文摘要 · Abstract (English)
Retrieval of previously fact-checked claims is a well-established task, whose automation can assist professional fact-checkers in the initial steps of information verification. Previous works have mostly tackled the task monolingually, i.e., having both the input and the retrieved claims in the same language. However, especially for languages with a limited availability of fact-checks and in case of global narratives, such as pandemics, wars, or international politics, it is crucial to be able to retrieve claims across languages. In this work, we examine strategies to improve the multilingual and crosslingual performance, namely selection of negative examples (in the supervised) and re-ranking (in the unsupervised setting). We evaluate all approaches on a dataset containing posts and claims in 47 languages (283 language combinations). We observe that the best results are obtained by using LLM-based re-ranking, followed by fine-tuning with negative examples sampled using a sentence similarity-based strategy. Most importantly, we show that crosslinguality is a setup with its own unique characteristics compared to the multilingual setup.
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