Our paper describing JoeyNMT was recently accepted at EMNLP so we thought it would be a good time to present our project to a larger community. Originally starting as a way to introduce students to neural machine translation methods without having to explain the intricacies of state of the art systems, JoeyNMT has now been in use for the past year now within our research group as a baseline system that is easily hackable and expandable. It has also found use Indaba Deep Learning school in Kenya and is a core tool used in the masakhane.io project to train NMT on African Languages.

Right now we have implemented

• RNNs (LSTM/GRU) and transformers for encoding and decoding
• Multiple attention models (MLP, Dot, Multi-head, and bilinear)
• character, word-level, and byte-pair encoded inputs
• Greedy decoding and beam search

Baseline models are available for English->{German, Latvian, Afrikaans, Zulu, Xitsonga, Northern Sotho, Setswana, isiZulu}

We have a github, blog post, and paper for JoeyNMT. We’d love to have more contributors and cover more language pairs.