Emotion Flip Reasoning in Multiparty Conversations

In a conversational dialogue, speakers may have different emotional states and their dynamics play an important role in understanding dialogue's emotional discourse. However, simply detecting emotions is not sufficient to entirely comprehend the speaker-specific changes in emotion that occur during a conversation. To understand the emotional dynamics of speakers in an efficient manner, it is imperative to identify the rationale or instigator behind any changes or flips in emotion expressed by the speaker. In this article, we explore the task called instigator-based emotion flip reasoning (EFR), which aims to identify the instigator behind a speaker's emotion flip within a conversation. For example, an emotion flip from
joy
to
anger
could be caused by an instigator like
threat
. To facilitate this task, we present MELD-I, a dataset that includes ground-truth EFR instigator labels, which are in line with emotional psychology. To evaluate the dataset, we propose a novel neural architecture called
TGIF
, which leverages Transformer encoders and stacked GRUs to capture the dialogue context, speaker dynamics, and emotion sequence in a conversation. Our evaluation demonstrates the state-of-the-art performance (+4%–12% increase in F1-score) against five baselines used for the task. Further, we establish the generalizability of
TGIF
on an unseen dataset in a zero-shot setting. In addition, we provide a detailed analysis of the competing models, highlighting the advantages and limitations of our neural architecture.