  Labeling corpora constitutes a bottleneck to create models for new tasks or
domains. Large language models mitigate the issue with automatic corpus
labeling methods, particularly for categorical annotations. Some NLP tasks such
as emotion intensity prediction, however, require text regression, but there is
no work on automating annotations for continuous label assignments. Regression
is considered more challenging than classification: The fact that humans
perform worse when tasked to choose values from a rating scale lead to
comparative annotation methods, including best-worst scaling. This raises the
question if large language model-based annotation methods show similar
patterns, namely that they perform worse on rating scale annotation tasks than
on comparative annotation tasks. To study this, we automate emotion intensity
predictions and compare direct rating scale predictions, pairwise comparisons
and best-worst scaling. We find that the latter shows the highest reliability.
A transformer regressor fine-tuned on these data performs nearly on par with a
model trained on the original manual annotations.
