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.

翻译：人与人之间最有意义的联系往往通过分享个人叙事中的共同脆弱性和情感经历而建立。我们提出了一项新任务，即基于移情共鸣来识别个人故事中的相似性——即两人对彼此经历产生共情的程度，而非自然语言处理领域主流的原始语义或词汇相似性。借鉴社会心理学洞见，我们构建了一个框架，将移情相似性操作化为故事的三个关键特征：主要事件、情感轨迹以及整体寓意。我们创建了移情叙事数据集，包含1,500个标注了移情相似性特征的个人故事，以及2,000对标注了移情相似性分数的故事对。利用该数据集，我们微调了一个模型来计算故事对的移情相似性，并证明其在自动相关性评估和检索指标上优于语义相似性模型。通过一项150人参与的用户研究，我们评估了该模型在检索用户能共情的故事方面的效果（与基于朴素语义相似性的检索对比），发现参与者对模型检索到的故事表现出显著更高的共情水平。我们的工作对运用共情感知模型促进人际连接与共情具有重要启示。