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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 131140 of 141 papers

TitleStatusHype
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversationsCode0
NELEC at SemEval-2019 Task 3: Think Twice Before Going DeepCode0
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationCode0
Context-Dependent Embedding Utterance Representations for Emotion Recognition in ConversationsCode0
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion RecognitionCode0
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue SystemsCode0
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion RecognitionCode0
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory NetworkCode0
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversationCode0
Recurrent Convolutional Neural Networks for Text ClassificationCode0
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