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Prior research has shown that typical fact-checking models for stand-aloneclaims struggle with claims made in dialogues. As a solution, fine-tuning thesemodels on labelled dialogue data has been proposed. However, creating separatemodels for each use case is impractical, and we show that fine-tuning modelsfor dialogue results in poor performance on typical fact-checking. To overcomethis challenge, we present techniques that allow us to use the same models forboth dialogue and typical fact-checking. These mainly focus on retrievaladaptation and transforming conversational inputs so that they can beaccurately predicted by models trained on stand-alone claims. We demonstratethat a typical fact-checking model incorporating these techniques iscompetitive with state-of-the-art models fine-tuned for dialogue, whilemaintaining its accuracy on stand-alone claims.

Automated Fact-Checking in Dialogue: Are Specialized Models Needed?