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
DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
zerohd4869/DialogueCRN
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ACL 2021
Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
declare-lab/knot
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COLING 2022
We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network.
UniSA: Unified Generative Framework for Sentiment Analysis
dawn0815/UniSA
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Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information.
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
Recurrent Convolutional Neural Networks for Text Classification
mindspore-ai/models
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Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence 2015
The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
SenticNet/conv-emotion
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NAACL 2018
Emotion recognition in conversations is crucial for the development of empathetic machines.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
SenticNet/conv-emotion
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EMNLP 2018
Emotion recognition in conversations is crucial for building empathetic machines.
Integrating Recurrence Dynamics for Speech Emotion Recognition
We investigate the performance of features that can capture nonlinear recurrence dynamics embedded in the speech signal for the task of Speech Emotion Recognition (SER).
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
chenyangh/SemEval2019Task3
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SEMEVAL 2019
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext.
NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep
iamgroot42/nelec
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SEMEVAL 2019
The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance.
