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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 81–90 of 141 papers

TitleStatusHype
Structure-Aware Transformer for Graph Representation LearningCode2
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation—0
Hybrid Curriculum Learning for Emotion Recognition in Conversation—0
Contrast and Generation Make BART a Good Dialogue Emotion RecognizerCode1
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation—0
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological KnowledgeCode1
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation—0
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical NetworksCode1
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