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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 121–130 of 141 papers

TitleStatusHype
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation—0
SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation—0
ERNetCL: A novel emotion recognition network in textual conversation based on curriculum learning strategy—0
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement—0
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation—0
Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation—0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Static and Dynamic Speaker Modeling based on Graph Neural Network for Emotion Recognition in Conversation—0
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