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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 71–80 of 141 papers

TitleStatusHype
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation—0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition—0
MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting—0
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog—0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation—0
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