  Personal Narratives (PN) - recollections of facts, events, and thoughts from
one's own experience - are often used in everyday conversations. So far, PNs
have mainly been explored for tasks such as valence prediction or emotion
classification (e.g. happy, sad). However, these tasks might overlook more
fine-grained information that could prove to be relevant for understanding PNs.
In this work, we propose a novel task for Narrative Understanding: Emotion
Carrier Recognition (ECR). Emotion carriers, the text fragments that carry the
emotions of the narrator (e.g. loss of a grandpa, high school reunion), provide
a fine-grained description of the emotion state. We explore the task of ECR in
a corpus of PNs manually annotated with emotion carriers and investigate
different machine learning models for the task. We propose evaluation
strategies for ECR including metrics that can be appropriate for different
tasks.
