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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 76–100 of 141 papers

TitleStatusHype
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and ParaphrasingCode0
MONAH: Multi-Modal Narratives for Humans to analyze conversationsCode0
NELEC at SemEval-2019 Task 3: Think Twice Before Going DeepCode0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory NetworkCode0
Recurrent Convolutional Neural Networks for Text ClassificationCode0
SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)Code0
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationCode0
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal AlignmentCode0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—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
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
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation—0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation—0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting—0
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations—0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation—0
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations—0
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