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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 5160 of 141 papers

TitleStatusHype
How to Enhance Causal Discrimination of Utterances: A Case on Affective ReasoningCode1
Context-Dependent Embedding Utterance Representations for Emotion Recognition in ConversationsCode0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition0
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
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in ConversationCode1
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
Deep Emotion Recognition in Textual Conversations: A SurveyCode0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations0
Distribution-based Emotion Recognition in ConversationCode1
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