A modular RL library to fine-tune language models to human preferences
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Updated
Mar 1, 2024 - Python
A modular RL library to fine-tune language models to human preferences
RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
This repository contains a new generative model of chatbot based on seq2seq modeling.
Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
Conversational AI tooling & personas built on Cohere's LLMs
A PyTorch Implementation of japanese chatbot using BERT and Transformer's decoder
This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.
The implementation of the paper "Augmenting Neural Response Generation with Context-Aware Topical Attention"
Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090
Generating responses with pretrained XLNet and GPT-2 in PyTorch.
Source code for the paper "Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering".
MoEL: Mixture of Empathetic Listeners
Code for the paper Code for the paper InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning
Official repository of the AAAI'2022 paper "GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection"
Code for "Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation". [AAAI 2021]
🥤🧑🏻🚀Code and dataset for our EMNLP 2023 paper - "SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization"
Code for ACL 2021 main conference paper "Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances".
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