Prior research has shown that typical fact-checking models for stand-alone claims struggle with claims made in dialogues. As a solution, fine-tuning these models on labelled dialogue data has been proposed. However, creating separate models for each use case is impractical, and we show that fine-tuning models for dialogue results in poor performance on typical fact-checking. To overcome this challenge, we present techniques that allow us to use the same models for both dialogue and typical fact-checking. These mainly focus on retrieval adaptation and transforming conversational inputs so that they can be accurately predicted by models trained on stand-alone claims. We demonstrate that a typical fact-checking model incorporating these techniques is competitive with state-of-the-art models fine-tuned for dialogue, while maintaining its accuracy on stand-alone claims.

翻译：先前研究表明，针对独立声明进行典型事实核查的模型在处理对话中的声明时表现不佳。为此，有研究者提出在标注的对话数据上微调这些模型作为解决方案。然而，为每种用例创建独立模型并不现实，且我们证明了针对对话微调后的模型在典型事实核查任务中表现较差。为克服这一挑战，我们提出若干技术手段，使得同一模型既可处理对话场景又可用于典型事实核查。这些技术主要聚焦于检索适配与对话输入转换，从而使基于独立声明训练的模型能够准确预测对话类输入。我们证实，融合这些技术的典型事实核查模型，在与经对话微调的最先进模型竞争时表现出同等竞争力，同时仍保持其在独立声明上的准确性。