  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.
