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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

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Showing 126–141 of 141 papers

TitleStatusHype
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances—0
NELEC at SemEval-2019 Task 3: Think Twice Before Going DeepCode0
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERTCode0
Integrating Recurrence Dynamics for Speech Emotion RecognitionCode0
DialogueRNN: An Attentive RNN for Emotion Detection in ConversationsCode1
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in ConversationsCode0
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection—0
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos—0
Context-Dependent Sentiment Analysis in User-Generated VideosCode0
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumCode1
Bag of Tricks for Efficient Text ClassificationCode1
Recurrent Neural Network for Text Classification with Multi-Task Learning—0
Recurrent Convolutional Neural Networks for Text ClassificationCode0
Convolutional Neural Networks for Sentence ClassificationCode1
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