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Emotion Recognition in Conversation

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평가표: 16
보존된 평가 기록: 227

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. .

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/LIN-SHANG/InstructERC

공식 코드

InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

https://github.com/LiXinran6/LSDGNN_ICL

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Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

https://github.com/Maxwe11y/BiERU

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BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis

https://github.com/SenticNet/conv-emotion

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DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

https://github.com/SpectData/MONAH

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MONAH: Multi-Modal Narratives for Humans to analyze conversations

https://github.com/alibabaresearch/damo-convai

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Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment

https://github.com/amanshenoy/multilogue-net

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Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

https://github.com/bioinf-jku/TTUR

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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/blacknoodle/tucore-gcn

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Graph Based Network with Contextualized Representations of Turns in Dialogue

https://github.com/borgwardtlab/sat

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Structure-Aware Transformer for Graph Representation Learning

https://github.com/butterfliesss/sdt

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A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

https://github.com/caskcsg/spcl

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Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

https://github.com/chenyangh/SemEval2019Task3

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ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

https://github.com/dawn0815/UniSA

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UniSA: Unified Generative Framework for Sentiment Analysis

https://github.com/dawn0815/saeval-benchmark

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UniSA: Unified Generative Framework for Sentiment Analysis

https://github.com/declare-lab/MELD

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MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

https://github.com/declare-lab/conv-emotion

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COSMIC: COmmonSense knowledge for eMotion Identification in Conversations

https://github.com/declare-lab/knot

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KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks

https://github.com/declare-lab/sinkhorn-loss

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KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks

https://github.com/deqingyang/cisper

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Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation

https://github.com/etzinis/nldrp

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Integrating Recurrence Dynamics for Speech Emotion Recognition

https://github.com/exploration-lab/cogmen

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COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

https://github.com/facebookresearch/fastText

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Bag of Tricks for Efficient Text Classification

https://github.com/gguibon/protoseq

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Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks

https://github.com/google-research/bert

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/hujingwen6666/MMGCN

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MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

https://github.com/iamgroot42/nelec

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NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

https://github.com/lcs2-iiitd/emnlp-coffee

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From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues

https://github.com/lemei/affective-computing

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Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective

https://github.com/lemei/unimse

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UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

https://github.com/leqsnan/skaig-erc

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A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation

https://github.com/lijfrank-open/CFN-ESA

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CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition

https://github.com/lijfrank-open/EmotionIC

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EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation

https://github.com/lijfrank-open/GA2MIF

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GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection

https://github.com/lijfrank-open/GraphCFC

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GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognition

https://github.com/lijfrank-open/GraphSmile

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Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

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