  Efficient discovery of a speaker's emotional states in a multi-party
conversation is significant to design human-like conversational agents. During
a conversation, the cognitive state of a speaker often alters due to certain
past utterances, which may lead to a flip in their emotional state. Therefore,
discovering the reasons (triggers) behind the speaker's emotion-flip during a
conversation is essential to explain the emotion labels of individual
utterances. In this paper, along with addressing the task of emotion
recognition in conversations (ERC), we introduce a novel task - Emotion-Flip
Reasoning (EFR), that aims to identify past utterances which have triggered
one's emotional state to flip at a certain time. We propose a masked memory
network to address the former and a Transformer-based network for the latter
task. To this end, we consider MELD, a benchmark emotion recognition dataset in
multi-party conversations for the task of ERC, and augment it with new
ground-truth labels for EFR. An extensive comparison with five state-of-the-art
models suggests improved performances of our models for both tasks. We further
present anecdotal evidence and both qualitative and quantitative error analyses
to support the superiority of our models compared to the baselines.
