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 individual's 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.

该研究提出了一种基于 And-or 图的模型，名为 SocAoG，用于从对话中推断社交关系，包括从语义对话预测属性和关系、基于相关属性更新社交关系以及基于人际社交关系更新个体属性，并在 DialogRE 和 MovieGraph 上进行了实证研究和案例研究。