多语言与跨语言检索已核查事实声明：两种方法的故事
Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches
AI总结：
本文研究多语言与跨语言事实核查声明检索，通过负例选择和LLM重排序策略，在47种语言数据集上验证了LLM重排序加句子相似度负例微调效果最佳，并揭示跨语言设置具有独特特征。
摘要：
检索先前已核查的事实声明是一项成熟的任务，其自动化可以在信息核实的最初步骤中协助专业事实核查人员。以往的工作大多以单语言方式处理该任务，即输入和检索到的声明使用同一种语言。然而，特别是对于事实核查资源有限的语言，以及在涉及全球性叙事（如大流行病、战争或国际政治）的情况下，能够跨语言检索声明至关重要。在这项工作中，我们研究了改进多语言和跨语言性能的策略，即负例的选择（在有监督设置中）和重排序（在无监督设置中）。我们在一个包含47种语言（283种语言组合）的帖子和声明的数据集上评估了所有方法。我们观察到，最佳结果是通过使用基于大语言模型（LLM）的重排序获得的，其次是使用基于句子相似度策略采样的负例进行微调。最重要的是，我们表明，与多语言设置相比，跨语言是一种具有其自身独特特征的设置。
英文摘要：
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.
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。
