Context-Aware Multimodal Claim Verification in Spoken Dialogues
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 c...
| Authors | Chaewan Chun, Delvin Ce Zhang, Dongwon Lee |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 22 September 2026 |
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