Emotion Recognition
Leaderboards
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Subtasks
Greatest papers with code
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
IJCNLP 2019
• SenticNet/conv-emotion
• 
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling
Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for the target task of detecting emotions in context.
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
IJCNLP 2019
• SenticNet/conv-emotion
•
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
EMNLP 2018
• SenticNet/conv-emotion
•
Emotion recognition in conversations is crucial for building empathetic machines.
#2 best model for
Emotion Recognition in Conversation
on IEMOCAP
EMOTION RECOGNITION IN CONTEXT EMOTION RECOGNITION IN CONVERSATION MULTIMODAL EMOTION RECOGNITION MULTIMODAL SENTIMENT ANALYSIS
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
NAACL 2018
• SenticNet/conv-emotion
•
Emotion recognition in conversations is crucial for the development of empathetic machines.
#3 best model for
Emotion Recognition in Conversation
on IEMOCAP
EMOTION RECOGNITION IN CONTEXT EMOTION RECOGNITION IN CONVERSATION OPINION MINING
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
SOTA for 3D Facial Expression Recognition on 2017_test set (using extra training data)
3D FACIAL EXPRESSION RECOGNITION EMOTION RECOGNITION FACIAL LANDMARK DETECTION
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
Several lexica for sentiment analysis have been developed and made available in the NLP community.