Context-Aware Multimodal Claim Verification in Spoken Dialogues

Abstract:Spoken factual claims often occur within multi-turn conversations, where surrounding dialogue can provide context unavailable from the claim alone. Yet most fact-checking research evaluates isolated text, leaving conversational audio under-studied. We introduce MAD2, a synthetic Multi-turn Audio Dialogues benchmark for spoken claim verification with 1,000 two-speaker dialogues, 1,230 sentence-level check-worthy candidate annotations, and approximately 10 hours of audio. We also propose calibrated multimodal fusion of a context-aware audio encoder and a dialogue-aware text model. Adding dialogue context improves verification across settings, although the gains vary by scenario. Past-only context often approaches local offline performance, suggesting its usefulness when future context is unavailable. Fusion achieves its highest mean advantage over text with full-dialogue context, but does not consistently or significantly outperform text across settings. Under full-dialogue context, exploratory subgroup results show greater performance variation across dialogue scenarios for text and fusion, but across spread styles for audio.

Computation and Language (cs.CL); Social and Information Networks (cs.SI)

arXiv:2606.11420 [cs.CL]

(or arXiv:2606.11420v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.11420 Focus to learn more arXiv-issued DOI via DataCite

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