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Multimodal simultaneous NMT

This repository is a stripped down clone of the upstream nmtpytorch repository.


  • Julia Ive added all parts relating to reinforcement learning (RL) based simultaneous MT and MMT.
  • As part of her MSc. thesis, Veneta Haralampieva contributed layers & models for Transformers support to simultaneous MT and MMT.


The installation should be straightforward using anaconda. The below command will install the toolkit in develop mode into a newly created simnmt environment. This will allow your changes to the GIT checkout folder to be instantaneously reflected to the imported modules and executable scripts.

conda env create -f environment.yml

Unsupervised reward in RL for MT

Code for the paper:

Exploring Supervised and Unsupervised Rewards in Machine Translation. Julia Ive, Zixu Wang, Marina Fomicheva, Lucia Specia (2021). To appear in the Proceedings of EACL.

  1. Follow the guidelines above to install the main code

  2. Pre-train the actor (modify the paths in the config):

$ nmtpy train -C ./configs/unsupRL/en_de-cgru-nmt-bidir-base.conf
  1. Train SAC with the unsupervised reward (modify the paths in the config, pretrained_file indicates the location of the pre-trained Actor):
$ nmtpy train -C ./configs/unsupRL/en_de-cgru-nmt-bidir-diyan.conf

The implementation of the Soft Actor-Critic framework follows the architecture and style of the Deep-Reinforcement-Learning-Algorithms-with-PyTorch library, developed by Petros Christodoulou.


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