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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 101–110 of 141 papers

TitleStatusHype
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations—0
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos—0
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation—0
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition—0
Contextualized Emotion Recognition in Conversation as Sequence Tagging—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations—0
Neural Feature Extraction for Contextual Emotion Detection—0
NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation—0
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