Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning

TL;DR

This paper introduces a novel approach to model emotional trajectories in stories using transformers and weakly-supervised learning, providing a new benchmark with continuous valence and arousal labels for children's stories.

Contribution

It creates a benchmark dataset with continuous emotion annotations and develops a fine-tuned DeBERTa model with weak supervision for emotional trajectory prediction.

Findings

Achieved CCC of 0.8221 for valence and 0.7125 for arousal.

Demonstrated the effectiveness of weakly-supervised learning in emotion modeling.

Analyzed factors affecting prediction variability and identified challenging examples.

Abstract

Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no benchmark for this task. We address this gap by introducing continuous valence and arousal labels for an existing dataset of children's stories originally annotated with discrete emotion categories. We collect additional annotations for this data and map the categorical labels to the continuous valence and arousal space. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. The best configuration achieves a Concordance Correlation Coefficient (CCC) of $.8221$ for valence and $.7125$ for arousal on the test set, demonstrating the efficacy of our proposed approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story. In addition, we uncover the weaknesses of our approach by investigating examples that prove to be difficult to predict.