  The valence analysis of speakers' utterances or written posts helps to
understand the activation and variations of the emotional state throughout the
conversation. More recently, the concept of Emotion Carriers (EC) has been
introduced to explain the emotion felt by the speaker and its manifestations.
In this work, we investigate the natural inter-dependency of valence and ECs
via a multi-task learning approach. We experiment with Pre-trained Language
Models (PLM) for single-task, two-step, and joint settings for the valence and
EC prediction tasks. We compare and evaluate the performance of generative
(GPT-2) and discriminative (BERT) architectures in each setting. We observed
that providing the ground truth label of one task improves the prediction
performance of the models in the other task. We further observed that the
discriminative model achieves the best trade-off of valence and EC prediction
tasks in the joint prediction setting. As a result, we attain a single model
that performs both tasks, thus, saving computation resources at training and
inference times.
