  The most meaningful connections between people are often fostered through
expression of shared vulnerability and emotional experiences in personal
narratives. We introduce a new task of identifying similarity in personal
stories based on empathic resonance, i.e., the extent to which two people
empathize with each others' experiences, as opposed to raw semantic or lexical
similarity, as has predominantly been studied in NLP. Using insights from
social psychology, we craft a framework that operationalizes empathic
similarity in terms of three key features of stories: main events, emotional
trajectories, and overall morals or takeaways. We create EmpathicStories, a
dataset of 1,500 personal stories annotated with our empathic similarity
features, and 2,000 pairs of stories annotated with empathic similarity scores.
Using our dataset, we fine-tune a model to compute empathic similarity of story
pairs, and show that this outperforms semantic similarity models on automated
correlation and retrieval metrics. Through a user study with 150 participants,
we also assess the effect our model has on retrieving stories that users
empathize with, compared to naive semantic similarity-based retrieval, and find
that participants empathized significantly more with stories retrieved by our
model. Our work has strong implications for the use of empathy-aware models to
foster human connection and empathy between people.
