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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 31–40 of 141 papers

TitleStatusHype
Accumulating Word Representations in Multi-level Context Integration for ERC TaskCode0
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in ConversationsCode1
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition—0
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed DialoguesCode0
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation—0
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language ModelsCode1
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement—0
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations—0
UniSA: Unified Generative Framework for Sentiment AnalysisCode1
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
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