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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 7180 of 141 papers

TitleStatusHype
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in ConversationsCode0
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
Hierarchical Transformer Network for Utterance-level Emotion Recognition0
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