Resources

Neural Machine Translation

If you want to learn more about neural machine translation, check out the following resources.

Tutorials

• The Annotated Transformer by Alexander Rush

• The Annotated Encoder-Decoder by Jasmijn Bastings

• Graham Neubig: Neural Machine Translation and Sequence-to-sequence Models: A Tutorial.

• Philipp Koehn: Neural Machine Translation.

• Video recording of Chris Manning’s lecture on “NMT and Models with Attention” at Stanford (2017)

• Huggingface NLP task guides: Translation

Publications

• NMT papers in the ACL anthology

• statmt.org survey of NMT publications

• THUNLP-MT MT reading list

Data

• WMT: The shared tasks of the yearly Conference on Machine Translation (WMT) provide lots of parallel data

• OPUS: The OPUS project collects publicly available parallel data and provides it to everyone on their website.

• Huggingface: datasets.

Toolkits

A comprehensive list of NMT toolkits, ordered by deep learning backends can be found here.

PyTorch

Here’s a collection of links that should help you get started or improve your coding skills with PyTorch:

• Introduction to PyTorch - YouTube Series

• PyTorch Tutorials

• PyTorch Distributed Overview

Git Versioning

Never worked with Git before? For the basics, check out this tutorial by Roger Dudler and for more advanced usage this one by Lars Vogel.