26 Open Source Dialogue Generation Software Projects

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.

A list of recent papers regarding dialogue generation

GoNorth is a story and content planning tool for RPGs and other open world games.

Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow

Code for "MojiTalk: Generating Emotional Responses at Scale" https://arxiv.org/abs/1711.04090

The implementation of the paper "Augmenting Neural Response Generation with Context-Aware Topical Attention"

Generating responses with pretrained XLNet and GPT-2 in PyTorch.

Dynamic Knowledge-Grounded Dialogue Generation

Repository to generate CLEVR-Dialog: A diagnostic dataset for Visual Dialog

Source code for the paper "Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering".

A pytorch implementation for the MMI-anti model

A Paper List for Open-Domain Dialogue Generation, and related datasets.

A PyTorch Implementation of japanese chatbot using BERT and Transformer's decoder

MoEL: Mixture of Empathetic Listeners

code for CIKM'18 long paper, Explicit state tracking with semi-supervision for neural dialogue generation

Record papers for some NLP related area

Source code for our EMNLP19 paper "Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework"

Dialogue Knowledge Transfer Networks (DiKTNet)

Code for EMNLP 2019 paper "Modeling Multi-Action Policy for Task-Oriented Dialogues"

Code for "Polite Dialogue Generation Without Parallel Data"

CVAE_XGate model in paper "Xu, Dusek, Konstas, Rieser. Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity"

The codebase for "Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation" (Cai et al., AAAI 2020)