https://github.com/ludybupt/FATRER
공식 코드FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition
Papers with Code 아카이브
Emotion Recognition in Conversation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 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/luftmenscher/RBA-GCN
공식 코드RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition
https://github.com/nlpwm-whu/mplp
공식 코드Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing
https://github.com/patricia-pereira/cd-erc
공식 코드Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations
https://github.com/patricia-pereira/deep-emotion-recognition-in-textual-conversations
공식 코드https://github.com/qwenlm/qwen-audio
공식 코드Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
https://github.com/rgop13/GRASP
공식 코드GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
https://github.com/rungjoo/Emotion_not_One
공식 코드https://github.com/rungjoo/compm
공식 코드https://github.com/scutcyr/CPED
공식 코드CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
https://github.com/shawx825/hidialog
공식 코드Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
https://github.com/shenwzh3/DAG-ERC
공식 코드Directed Acyclic Graph Network for Conversational Emotion Recognition
https://github.com/shenwzh3/DialogXL
공식 코드DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
https://github.com/tae898/erc
공식 코드EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa
https://github.com/tub-cv-group/conclugen
공식 코드Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
https://github.com/vanntc711/multidag-cl
공식 코드Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition
https://github.com/w-wu/erc-slt22
공식 코드https://github.com/whatissimondoing/cog-bart
공식 코드Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
https://github.com/wxjiao/AGHMN
공식 코드Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network
https://github.com/wxjiao/Pre-CODE
공식 코드Exploiting Unsupervised Data for Emotion Recognition in Conversations
https://github.com/wykstc/MERC-main
공식 코드Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
https://github.com/yingjie7/BiosERC
공식 코드BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks
https://github.com/yongaifadian1/Dynamic-Parameter-Memory_DPM
공식 코드https://github.com/yu-fangxu/eacl
공식 코드Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversation
https://github.com/yuntaeyang/telme
공식 코드TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation
https://github.com/zerohd4869/DialogueCRN
공식 코드DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations
https://github.com/zerohd4869/mm-dfn
공식 코드MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations
https://github.com/zerohd4869/sacl
공식 코드Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
https://github.com/zhongpeixiang/KET
공식 코드Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
https://github.com/zodiark-ch/mater-of-our-emnlp2023-paper
공식 코드How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning
https://gitlab.cs.uni-duesseldorf.de/general/dsml/emowoz-public
공식 코드https://github.com/1wy/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-192
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.