Natural Language Generation
A curated list of resources dedicated to Natural Language Generation (NLG), including datasets, libraries, tools, and research.
What is Natural Language Generation?
Awesome Natural Language Generation is a curated GitHub repository listing resources for Natural Language Generation (NLG). It compiles datasets, libraries, tools, research papers, and learning materials to help developers and researchers explore and implement NLG technologies. The list covers applications ranging from chatbots and story generation to data descriptions and evaluation metrics.
NLG researchers, AI/ML practitioners, data scientists, and developers building text-generation systems, chatbots, or narrative applications. It's also valuable for students and academics seeking structured learning resources in the field.
It provides a single, organized point of reference for the fragmented NLG landscape, saving time on resource discovery. Being community-driven and open-source, it stays updated with diverse, practical tools and cutting-edge research not found in proprietary platforms.
Overview
A curated list of resources dedicated to Natural Language Generation (NLG)
Use Cases
Best For
- Finding datasets for training NLG models like game summaries or weather forecasts
- Discovering open-source libraries for text generation and templating
- Researching neural NLG techniques and transformer-based models
- Building dialog systems or chatbots with NLG components
- Evaluating NLG outputs with automated metrics and tools
- Exploring narrative generation tools for creative writing or interactive stories
Not Ideal For
- Developers needing a single, integrated NLG API with commercial support and guaranteed uptime
- Teams on tight deadlines requiring plug-and-play text generation solutions with comprehensive documentation
- Beginners seeking step-by-step tutorials or guided learning paths for NLG implementation
- Projects that depend on actively maintained, version-controlled libraries with frequent updates and community support
Pros & Cons
Pros
Aggregates a wide range of NLG datasets, libraries, papers, and tools in one place, as evidenced by detailed sections like Datasets, Libraries, and Neural NLG, saving time on scattered searches.
Includes resources for varied NLG applications such as chatbots, story generation, and data-to-text systems, reflecting the broad spectrum highlighted in the README's contents list.
Links to both cutting-edge research papers (e.g., from 2022 on evaluation obstacles) and practical tools like Tracery and SimpleNLG, supporting academic and industrial use effectively.
Maintained as an open-source GitHub repository under a CC0 license, allowing for community contributions and updates, ensuring diversity and currentness in listed resources.
Cons
The list provides links without rating or reviewing resources, leaving users to independently vet each for reliability, maintenance status, and suitability for their projects.
While it catalogs tools and libraries, it doesn't offer tutorials, integration examples, or best practices, making it less useful for hands-on development without additional research.
As a curated list, some links may become outdated or unmaintained over time, requiring manual verification for currentness, which can be time-consuming.
Frequently Asked Questions
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