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EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition

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arxiv 2203.13504 v1 pith:UC7OTIRT submitted 2022-03-25 cs.CL cs.SDeess.AS

Zaijing Li , Fengxiao Tang , Ming Zhao , Yusen Zhu This is my paper
classification cs.CLcs.SDeess.AS
keywords emotionmodelcapsulecontextconversationemocapsemoformeremotional
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Causal Emotion Recognition in Conversation: Context Saturation and Discourse-Marker Evidence

    cs.CL 2026-01 conditional novelty 5.0 of 10

    Using only past turns, ERC accuracy saturates within 10–30 preceding utterances; hierarchical encoding and SenticNet add little once context is present, and Sad turns show the largest context benefit and fewer left-pe...

  2. ADMC: Attention-based Diffusion Model for Missing Modalities Feature Completion

    cs.AI 2025-07 conditional novelty 5.0 of 10

    An attention-based diffusion model that generates missing modality features, combined with independently trained extractors, achieves state-of-the-art emotion and intent recognition on IEMOCAP and MIntRec.

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