  We present a model to predict fine-grained emotions along the continuous
dimensions of valence, arousal, and dominance (VAD) with a corpus with
categorical emotion annotations. Our model is trained by minimizing the EMD
(Earth Mover's Distance) loss between the predicted VAD score distribution and
the categorical emotion distributions sorted along VAD, and it can
simultaneously classify the emotion categories and predict the VAD scores for a
given sentence. We use pre-trained RoBERTa-Large and fine-tune on three
different corpora with categorical labels and evaluate on EmoBank corpus with
VAD scores. We show that our approach reaches comparable performance to that of
the state-of-the-art classifiers in categorical emotion classification and
shows significant positive correlations with the ground truth VAD scores. Also,
further training with supervision of VAD labels leads to improved performance
especially when dataset is small. We also present examples of predictions of
appropriate emotion words that are not part of the original annotations.
