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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

Showing 110 of 141 papers

TitleStatusHype
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in ConversationCode0
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in ConversationCode0
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations0
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations0
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation0
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation0
Recent Trends of Multimodal Affective Computing: A Survey from NLP PerspectiveCode2
NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations0
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