  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 paper, 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 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.
Additionally, we provide a detailed analysis of the competing models,
highlighting the advantages and limitations of our neural architecture.
