  We examine a new commonsense reasoning task: given a narrative describing a
social interaction that centers on two protagonists, systems make inferences
about the underlying relationship trajectory. Specifically, we propose two
evaluation tasks: Relationship Outlook Prediction MCQ and Resolution Prediction
MCQ. In Relationship Outlook Prediction, a system maps an interaction to a
relationship outlook that captures how the interaction is expected to change
the relationship. In Resolution Prediction, a system attributes a given
relationship outlook to a particular resolution that explains the outcome.
These two tasks parallel two real-life questions that people frequently ponder
upon as they navigate different social situations: "where is this relationship
going?" and "how did we end up here?". To facilitate the investigation of human
social relationships through these two tasks, we construct a new dataset,
Social Narrative Tree, which consists of 1250 stories documenting a variety of
daily social interactions. The narratives encode a multitude of social elements
that interweave to give rise to rich commonsense knowledge of how relationships
evolve with respect to social interactions. We establish baseline performances
using language models and the accuracies are significantly lower than human
performance. The results demonstrate that models need to look beyond syntactic
and semantic signals to comprehend complex human relationships.
