  Stance detection is typically framed as predicting the sentiment in a given
text towards a target entity. However, this setup overlooks the importance of
the source entity, i.e., who is expressing the opinion. In this paper, we
emphasize the need for studying interactions among entities when inferring
stances. We first introduce a new task, entity-to-entity (E2E) stance
detection, which primes models to identify entities in their canonical names
and discern stances jointly. To support this study, we curate a new dataset
with 10,619 annotations labeled at the sentence-level from news articles of
different ideological leanings. We present a novel generative framework to
allow the generation of canonical names for entities as well as stances among
them. We further enhance the model with a graph encoder to summarize entity
activities and external knowledge surrounding the entities. Experiments show
that our model outperforms strong comparisons by large margins. Further
analyses demonstrate the usefulness of E2E stance detection for understanding
media quotation and stance landscape, as well as inferring entity ideology.
