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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 51–75 of 141 papers

TitleStatusHype
How to Enhance Causal Discrimination of Utterances: A Case on Affective ReasoningCode1
Context-Dependent Embedding Utterance Representations for Emotion Recognition in ConversationsCode0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition—0
EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversationCode0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in ConversationCode1
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
Deep Emotion Recognition in Textual Conversations: A SurveyCode0
Korean Drama Scene Transcript Dataset for Emotion Recognition in Conversations—0
Distribution-based Emotion Recognition in ConversationCode1
Supervised Prototypical Contrastive Learning for Emotion Recognition in ConversationCode1
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis—0
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation ExtractionCode1
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion DetectionCode1
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion RecognitionCode1
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
The Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in ConversationCode1
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation—0
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation—0
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AICode2
EmotionFlow: Capture the Dialogue Level Emotion TransitionsCode1
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