  Many machine learning tasks -- particularly those in affective computing --
are inherently subjective. When asked to classify facial expressions or to rate
an individual's attractiveness, humans may disagree with one another, and no
single answer may be objectively correct. However, machine learning datasets
commonly have just one "ground truth" label for each sample, so models trained
on these labels may not perform well on tasks that are subjective in nature.
Though allowing models to learn from the individual annotators' ratings may
help, most datasets do not provide annotator-specific labels for each sample.
To address this issue, we propose SeedBERT, a method for recovering annotator
rating distributions from a single label by inducing pre-trained models to
attend to different portions of the input. Our human evaluations indicate that
SeedBERT's attention mechanism is consistent with human sources of annotator
disagreement. Moreover, in our empirical evaluations using large language
models, SeedBERT demonstrates substantial gains in performance on downstream
subjective tasks compared both to standard deep learning models and to other
current models that account explicitly for annotator disagreement.
