This repository serves as a curated collection of research papers and resources focused on non-autoregressive generation (NAG) techniques, primarily for neural machine translation (NMT) and related sequence generation tasks. It aims to provide a comprehensive overview of the field's advancements for researchers and practitioners interested in faster, parallel sequence generation models.

How It Works

The project compiles a broad range of academic publications, categorizing them by conference and year. It highlights key methodologies and approaches within NAG, such as knowledge distillation, iterative refinement, latent variable models, and alignment learning, offering a structured overview of the evolution and diversification of NAG research.

Quick Start & Requirements

This repository is a collection of research papers and does not contain executable code for direct installation or running. The primary requirement is access to academic databases or pre-print servers (like arXiv) to retrieve the cited papers.

Highlighted Details

• Comprehensive bibliography spanning from 2018 to 2022.
• Covers diverse applications beyond NMT, including speech recognition and text editing.
• Features seminal papers and recent advancements in NAG methodologies.
• Organizes research by major NLP conferences (ACL, EMNLP, ICML, NAACL, etc.).

Maintenance & Community

The repository appears to be a static collection of references, with no explicit mention of active maintenance, community forums, or ongoing development. Contact information for Changhan Wang is provided.

Licensing & Compatibility

The repository itself, as a collection of links to research papers, is subject to the licensing of the individual papers and their respective publication venues. Compatibility for commercial use would depend on the licenses of the cited works.

Limitations & Caveats

This is a bibliography and not an implementation. Users must independently find and access the papers. The collection is limited to the papers cited in the README and may not be exhaustive of all NAG research.