  Emotion recognition in conversation (ERC) aims to analyze the speaker's state
and identify their emotion in the conversation. Recent works in ERC focus on
context modeling but ignore the representation of contextual emotional
tendency. In order to extract multi-modal information and the emotional
tendency of the utterance effectively, we propose a new structure named
Emoformer to extract multi-modal emotion vectors from different modalities and
fuse them with sentence vector to be an emotion capsule. Furthermore, we design
an end-to-end ERC model called EmoCaps, which extracts emotion vectors through
the Emoformer structure and obtain the emotion classification results from a
context analysis model. Through the experiments with two benchmark datasets,
our model shows better performance than the existing state-of-the-art models.
