  Predicting how events induce emotions in the characters of a story is
typically seen as a standard multi-label classification task, which usually
treats labels as anonymous classes to predict. They ignore information that may
be conveyed by the emotion labels themselves. We propose that the semantics of
emotion labels can guide a model's attention when representing the input story.
Further, we observe that the emotions evoked by an event are often related: an
event that evokes joy is unlikely to also evoke sadness. In this work, we
explicitly model label classes via label embeddings, and add mechanisms that
track label-label correlations both during training and inference. We also
introduce a new semi-supervision strategy that regularizes for the correlations
on unlabeled data. Our empirical evaluations show that modeling label semantics
yields consistent benefits, and we advance the state-of-the-art on an emotion
inference task.
