Affinity-based Interpretation of Triangle Social Scenarios

Computational interpretation of social scenarios is a critical step towards more human-like artificial intelligence. We present a model that interprets social scenarios by deducing the affinities of the constituent relationships. First, our model deploys Bayesian inference with an action affinity lexicon to infer probabilistic affinity relations characterizing the scenario. Subsequently, our model is able to use the inferred affinity relations to choose the most probable statement from multiple plausible statements about the scenario. We evaluate our approach on 80 Triangle-COPA multiple-choice problems that test interpretation of social scenarios. Our approach correctly answers the majority (59) of the 80 questions (73.75%), including questions about behaviors, emotions, social conventions, and complex constructs. Our model maintains interpretive power while using knowledge captured in the lightweight action affinity lexicon. Our model is a promising approach to interpretation of social scenarios, and we identify potential applications to automated narrative analysis, AI narrative generation, and assistive technology.