In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM's top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.
@article{arxiv.1706.00457,
title = {NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems},
author = {Ozan Caglayan and Mercedes García-Martínez and Adrien Bardet and Walid Aransa and Fethi Bougares and Loïc Barrault},
journal= {arXiv preprint arXiv:1706.00457},
year = {2018}
}