获取您的维生素C！基于对比证据的鲁棒事实验证
Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
摘要：
典型的事实验证模型使用检索到的书面证据来验证声明。然而，随着更多信息的收集和修订，证据来源往往会随着时间的推移而变化。为了适应，模型必须对支持证据的细微差异敏感。我们提出了VitaminC，一个充满挑战性案例的基准，需要事实验证模型来识别和调整轻微的事实变化。我们收集了超过100，000个维基百科修订版本，这些修订版本修改了一个潜在的事实，并利用这些修订版本，以及其他合成构建的版本，创建了总共超过400，000个声明-证据对。与以前的资源不同，维生素C中的例子是对比性的，即，它们包含的证据对在语言和内容上几乎完全相同，只是其中一个支持某一主张，而另一个不支持。我们表明，使用这种设计进行训练可以提高鲁棒性--在对抗性事实验证上提高了10%的准确性，在对抗性自然语言推理（NLI）上提高了6%。此外，维生素C的结构使我们为事实核查资源定义了额外的任务：标记证据中的相关词语以验证声明，识别事实修订，并通过事实一致的文本生成提供自动编辑。
英文摘要：
Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness -- improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.
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。
