This repository serves as a comprehensive roadmap and resource hub for Deep Generative Models (DGMs) applied to Natural Language Processing (NLP). It targets researchers and practitioners interested in understanding and implementing advanced probabilistic methods for language generation, representation learning, and structured prediction. The project aims to consolidate key concepts, seminal papers, and practical resources in this rapidly evolving field.

How It Works

The project is structured as a curated collection of links and references, organized by topic and chronology. It covers foundational DGM techniques like VAEs, GANs, and Normalizing Flows, alongside their applications in NLP tasks such as text generation, parsing, and semantic modeling. The roadmap also highlights recent advancements, including Transformers, State-Space Models, and Large Language Models, providing a historical perspective and a guide to current research frontiers.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable codebase. No installation or specific requirements are needed to browse its content.

Highlighted Details

• Extensive chronological overview of DGM and NLP milestones from 2013 to 2022.
• Detailed sections on core DGM techniques (VAEs, GANs, Flows, Score-based Models, Diffusion Models) with relevant papers.
• Comprehensive coverage of NLP applications including generation, decoding, structured prediction, and grammar induction.
• Dedicated sections on advanced topics like LLMs, State-Space Models, and emergent abilities.

Maintenance & Community

The repository is maintained by Yao Fu. It includes links to various seminars, courses, and books, suggesting a strong academic foundation and potential community engagement through shared learning resources.

Licensing & Compatibility

The repository itself is not licensed as code. The content is presented for informational purposes, with citations to original research papers.

Limitations & Caveats

This is a roadmap and resource compilation, not an executable library. Users will need to independently find, install, and implement the referenced models and techniques. The field is rapidly advancing, so some information may become dated.