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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
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
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition—0
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—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
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations—0
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