ViWikiFC：基于越南维基百科的文本知识源事实核查
ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source
AI总结：
本文提出ViWikiFC，首个越南维基百科事实核查开放域语料库，通过20000多个声明验证，采用BM25和InfoXLM(Large)模型在证据检索和判断预测中取得最佳结果。
摘要：
事实核查对于媒体生态系统中信息爆炸至关重要。尽管所有语言和国家都存在虚假信息，但大多数研究集中在英语和中文等大规模社区。低资源语言如越南语需要探索事实验证的语料库和模型。为此，我们构建了ViWikiFC，首个手动标注的开放域语料库，用于越南维基百科事实核查超过20000个声明，通过转换自维基百科文章提取的证据句子。我们通过多种语言学方面分析了该语料库，包括新的依赖率、新的n-gram率和新的词率。我们进行了各种越南事实核查实验，包括证据检索和判断预测。BM25和InfoXLM（Large）在两个任务中取得了最佳结果，BM25在SUPPORTS任务中达到88.30%的准确率，在REFUTES任务中达到86.93%，在NEI标签任务中仅达到56.67%。InfoXLM（Large）在证据检索任务中达到了86.51%的F1分数。此外，我们还进行了一种管道方法，使用InfoXLM（Large）和BM25时，仅达到67.00%的严格准确率。这些结果表明，我们的数据集对越南语言模型在事实核查任务中具有挑战性。
英文摘要：
Fact-checking is essential due to the explosion of misinformation in the media ecosystem. Although false information exists in every language and country, most research to solve the problem mainly concentrated on huge communities like English and Chinese. Low-resource languages like Vietnamese are necessary to explore corpora and models for fact verification. To bridge this gap, we construct ViWikiFC, the first manual annotated open-domain corpus for Vietnamese Wikipedia Fact Checking more than 20K claims generated by converting evidence sentences extracted from Wikipedia articles. We analyze our corpus through many linguistic aspects, from the new dependency rate, the new n-gram rate, and the new word rate. We conducted various experiments for Vietnamese fact-checking, including evidence retrieval and verdict prediction. BM25 and InfoXLM (Large) achieved the best results in two tasks, with BM25 achieving an accuracy of 88.30% for SUPPORTS, 86.93% for REFUTES, and only 56.67% for the NEI label in the evidence retrieval task, InfoXLM (Large) achieved an F1 score of 86.51%. Furthermore, we also conducted a pipeline approach, which only achieved a strict accuracy of 67.00% when using InfoXLM (Large) and BM25. These results demonstrate that our dataset is challenging for the Vietnamese language model in fact-checking tasks.
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。
