Predicting Evoked Emotions in Conversations
Enas Altarawneh Ameeta Agrawal Michael Jenkin Manos Papagelis
Understanding and predicting the emotional trajectory in multi-party
multi-turn conversations is of great significance. Such information can be
used, for example, to generate empathetic response in human-machine interaction
or to inform models of pre-emptive toxicity detection. In this work, we
introduce the novel problem of Predicting Emotions in Conversations (PEC) for
the next turn (n+1), given combinations of textual and/or emotion input up to
turn n. We systematically approach the problem by modeling three dimensions
inherently connected to evoked emotions in dialogues, including (i) sequence
modeling, (ii) self-dependency modeling, and (iii) recency modeling. These
modeling dimensions are then incorporated into two deep neural network
architectures, a sequence model and a graph convolutional network model. The
former is designed to capture the sequence of utterances in a dialogue, while
the latter captures the sequence of utterances and the network formation of
multi-party dialogues. We perform a comprehensive empirical evaluation of the
various proposed models for addressing the PEC problem. The results indicate
(i) the importance of the self-dependency and recency model dimensions for the
prediction task, (ii) the quality of simpler sequence models in short
dialogues, (iii) the importance of the graph neural models in improving the
predictions in long dialogues.