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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 61–70 of 141 papers

TitleStatusHype
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations—0
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog—0
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition—0
EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition—0
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations—0
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation—0
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation—0
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