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ConSK-GCN

The PyTorch code for paper: "CONSK-GCN: Conversational Semantic- and Knowledge-Oriented Graph Convolutional Network for Multimodal Emotion Recognition."

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conv-emotion

This repo contains implementation of different architectures for emotion recognition in conversations.

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annotated_deep_learning_paper_implementations

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

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nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

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DialogXL

The PyTorch implementaion for AAAI-2021 accepted paper DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition.

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DeepLearning

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TodKat

Transformer encoder-decoder for emotion detection in dialogues

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LSTM-Attention-based-Generative-Chat-bot

Personified Generative Chatbot using RNNs (LSTM) & Attention in TensorFlow

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Seq2seq-Chatbot-for-Keras

This repository contains a new generative model of chatbot based on seq2seq modeling.

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data-science-notebook

:book: 每一个伟大的**和行动都有一个微不足道的开始

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ailearning

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

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mtl-eadrg

Emotion-Aware Dialogue Response Generation by Multi-Task Learning

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Emotion-Recognition-in-Conversations

User Emotion Recognition and Response Generation in Dialogue Text

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DialogueCRN

Source code for ACL-IJCNLP 2021 paper "DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations"

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dialogue-emotion

Hierarchical Attention for Dialogue Emotion Classification (SemEval, NAACL)

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dialogue_gcn

Pytorch implementation to paper "DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation".

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personal-emotional-dialogue-system

paper list for dialogue system

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DialogueGenerationWithEmotion

Response generation giving specific emotion.

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XAI-Emotionally-Supportive-Conversations

This model primarily generates emotionally supportive dialogue in response to the user input, whilst it infers the type of user’s emotion, its intensity and the treatment strategy as a rationale.

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