Sequence-to-Sequence Language Models for Character and Emotion Detection
  in Dream Narratives
Gustave Cortal
The study of dreams has been central to understanding human
(un)consciousness, cognition, and culture for centuries. Analyzing dreams
quantitatively depends on labor-intensive, manual annotation of dream
narratives. We automate this process through a natural language
sequence-to-sequence generation framework. This paper presents the first study
on character and emotion detection in the English portion of the open DreamBank
corpus of dream narratives. Our results show that language models can
effectively address this complex task. To get insight into prediction
performance, we evaluate the impact of model size, prediction order of
characters, and the consideration of proper names and character traits. We
compare our approach with a large language model using in-context learning. Our
supervised models perform better while having 28 times fewer parameters. Our
model and its generated annotations are made publicly available.