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Emotion Recognition in Conversation

Given the transcript of a conversation along with speaker information of each constituent utterance, the ERC task aims to identify the emotion of each utterance from several pre-defined emotions. Formally, given the input sequence of N number of utterances [(u1, p1), (u2, p2), . . . , (uN , pN )], where each utterance ui = [ui,1, ui,2, . . . , ui,T ] consists of T words ui,j and spoken by party pi, the task is to predict the emotion label ei of each utterance ui. .

Papers

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Showing 81–90 of 141 papers

TitleStatusHype
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition—0
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog—0
Hierarchical Transformer Network for Utterance-level Emotion Recognition—0
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations—0
Hybrid Curriculum Learning for Emotion Recognition in Conversation—0
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection—0
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations—0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations—0
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