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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 121130 of 141 papers

TitleStatusHype
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition0
Hierarchical Transformer Network for Utterance-level Emotion Recognition0
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory NetworkCode0
Conversational Transfer Learning for Emotion Recognition0
Knowledge-Enriched Transformer for Emotion Detection in Textual ConversationsCode0
Neural Feature Extraction for Contextual Emotion Detection0
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations0
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations0
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationCode0
ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion DetectorCode0
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