  Telling stories is an integral part of human communication which can evoke
emotions and influence the affective states of the audience. Automatically
modelling 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 labelled benchmark for
this task. We address this gap by introducing continuous valence and arousal
annotations for an existing dataset of children's stories annotated with
discrete emotion categories. We collect additional annotations for this data
and map the originally categorical labels to the valence and arousal space.
Leveraging recent advances in Natural Language Processing, we propose a set of
novel Transformer-based methods for predicting valence and arousal signals over
the course of written stories. We explore several strategies for fine-tuning a
pretrained ELECTRA model and study the benefits of considering a sentence's
context when inferring its emotionality. Moreover, we experiment with
additional LSTM and Transformer layers. The best configuration achieves a
Concordance Correlation Coefficient (CCC) of .7338 for valence and .6302 for
arousal on the test set, demonstrating the suitability of our proposed
approach. Our code and additional annotations are made available at
https://github.com/lc0197/emotion_modelling_stories.
