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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 91100 of 141 papers

TitleStatusHype
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversationCode0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation0
Deep Emotion Recognition in Textual Conversations: A SurveyCode0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations0
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis0
Emotion Recognition in Conversation using Probabilistic Soft Logic0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation0
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