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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
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTaCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion RecognitionCode1
Context-Dependent Sentiment Analysis in User-Generated VideosCode0
Context-Dependent Embedding Utterance Representations for Emotion Recognition in ConversationsCode0
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversationCode0
ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion DetectorCode0
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in ConversationsCode0
Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and ParaphrasingCode0
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