Speech and Natural Language Processing > Natural Language Generation
Generation of text used in data-to-text, conversational agents, and narrative generation applications.
Contents
Datasets
A dataset for NLG in dialogue systems in the public transport information domain.
This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores.
The repository contains the code along with the required corpora that were used in order to build a system that "learns" how to generate English biographies for Semantic Web triples.
Dialog
Generate datasets for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL!
NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models.
This is the Plato Research Dialogue System, a flexible platform for developing conversational AI agents.
Evaluation
Grammar
Libraries
Narrative Generation
Neural Natural Language Generation
A robust Python tool for text-based AI training and generation using GPT-2.
Graph to sequence implemented in Pytorch combining Graph convolutional networks and opennmt-py.
A Neural Network based generative model for captioning images using Tensorflow.
A minimalistic codebase for finetuning and interacting with NLG models using PyTorch Lightning.
We present a PaperRobot who performs as an automatic research assistant.
Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.
Question generation is the task of automatically generating questions from a text paragraph.
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks.
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
This is a project allows people to train a variant of GPT-2 that makes up words, definitions and examples from scratch.