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This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadicinteractions, where interlocutors perform competitive and collaborative taskswith different behavior elicitation and cognitive workload. The datasetconsists of 90.5 hours of dyadic interactions among 147 participantsdistributed in 188 sessions, recorded using multiple audiovisual andphysiological sensors. Currently, it includes sociodemographic, self- andpeer-reported personality, internal state, and relationship profiling fromparticipants. As an initial analysis on UDIVA, we propose a transformer-basedmethod for self-reported personality inference in dyadic scenarios, which usesaudiovisual data and different sources of context from both interlocutors toregress a target person's personality traits. Preliminary results from anincremental study show consistent improvements when using all available contextinformation.

Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset