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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 1120 of 141 papers

TitleStatusHype
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in ConversationsCode1
A Facial Expression-Aware Multimodal Multi-task Learning Framework for Emotion Recognition in Multi-party ConversationsCode1
Contrast and Generation Make BART a Good Dialogue Emotion RecognizerCode1
How to Enhance Causal Discrimination of Utterances: A Case on Affective ReasoningCode1
Contextual Information and Commonsense Based Prompt for Emotion Recognition in ConversationCode1
Convolutional Neural Networks for Sentence ClassificationCode1
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC TasksCode1
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion RecognitionCode1
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion RecognitionCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
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