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

EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition