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.

本篇研究提出一种基于共情共鸣的个人故事相似度计算方法，通过对个人故事中的主要事件、情感轨迹和总体价值等三个特征进行操作来得出共情相似度得分，并使用 1500 个故事和 2000 对故事进行了数据集制作与模型的训练，最终证实了基于共情的相似度计算模型不仅在数据检索表现方面媲美基于词汇语义的模型，还能有效地促进人与人之间的情感共鸣与联系。

建模个人叙述中的共情相似性

Modeling Empathic Similarity in Personal Narratives

Sharing personal narratives is a fundamental aspect of human social behavior
as it helps share our life experiences. We can tell stories and rely on our
background to understand their context, similarities, and differences. A
substantial effort has been made towards developing storytelling machines or
inferring characters' features. However, we don't usually find models that
compare narratives. This task is remarkably challenging for machines since
they, as sometimes we do, lack an understanding of what similarity means. To
address this challenge, we first introduce a corpus of real-world spoken
personal narratives comprising 10,296 narrative clauses from 594 video
transcripts. Second, we ask non-narrative experts to annotate those clauses
under Labov's sociolinguistic model of personal narratives (i.e., action,
orientation, and evaluation clause types) and train a classifier that reaches
84.7% F-score for the highest-agreed clauses. Finally, we match stories and
explore whether people implicitly rely on Labov's framework to compare
narratives. We show that actions followed by the narrator's evaluation of these
are the aspects non-experts consider the most. Our approach is intended to help
inform machine learning methods aimed at studying or representing personal
narratives.

通过引入个人叙述语料库并使用 Labov 的社会语言学模型及机器学习方法进行标注，文章探讨了人们比较故事时依赖于行动和评价这两个方面，以此为基础帮助机器学习研究或表现个人叙述的方法。