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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 4150 of 141 papers

TitleStatusHype
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in ConversationCode1
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in ConversationsCode1
Directed Acyclic Graph Network for Conversational Emotion RecognitionCode1
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in ConversationCode1
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion RecognitionCode1
Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in ConversationsCode1
Exploiting Unsupervised Data for Emotion Recognition in ConversationsCode1
Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in ConversationCode1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in ConversationCode1
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