SOTAVerified

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 2130 of 141 papers

TitleStatusHype
Supervised Adversarial Contrastive Learning for Emotion Recognition in ConversationsCode1
How to Enhance Causal Discrimination of Utterances: A Case on Affective ReasoningCode1
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in ConversationCode1
Distribution-based Emotion Recognition in ConversationCode1
Supervised Prototypical Contrastive Learning for Emotion Recognition in ConversationCode1
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
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
Show:102550
← PrevPage 3 of 15Next →

No leaderboard results yet.