Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an $alpha$-$beta$-$gamma$ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an $alpha$ process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a $beta$ process updating the social relations based on related attributes, and (iii) a $gamma$ process updating individuals attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.
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