Emotion Recognition in Conversation
72 papers with code • 12 benchmarks • 14 datasets
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. .
Benchmarks
These leaderboards are used to track progress in Emotion Recognition in Conversation
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Libraries
Use these libraries to find Emotion Recognition in Conversation models and implementations
IEMOCAP
DailyDialog
MELD
EmoryNLP
SEMAINE
EmotionLines
EmoContext
CPED
CMU-MOSI
EmoWOZ
Most implemented papers
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector
This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text.
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
baaesh/semeval19_task3
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SEMEVAL 2019
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem.
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
zhongpeixiang/KET
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IJCNLP 2019
Messages in human conversations inherently convey emotions.
Conversational Transfer Learning for Emotion Recognition
We propose an approach, TL-ERC, where we pre-train a hierarchical dialogue model on multi-turn conversations (source) and then transfer its parameters to a conversational emotion classifier (target).
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
wxjiao/AGHMN
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We propose an Attention Gated Hierarchical Memory Network (AGHMN) to address the problems of prior work: (1) Commonly used convolutional neural networks (CNNs) for utterance feature extraction are less compatible in the memory modules; (2) Unidirectional gated recurrent units (GRUs) only allow each historical utterance to have context before it, preventing information propagation in the opposite direction; (3) The Soft Attention for summarizing loses the positional and ordering information of memories, regardless of how the memory bank is built.
Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation
amanshenoy/multilogue-net
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arXiv preprint 2020
Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions.
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
Maxwe11y/BiERU
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Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e. g., sentiment analysis, recommender systems, and human-robot interaction.
Exploiting Unsupervised Data for Emotion Recognition in Conversations
wxjiao/Pre-CODE
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Findings of the Association for Computational Linguistics 2020
Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task.
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
declare-lab/conv-emotion
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Findings of the Association for Computational Linguistics 2020
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
