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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 topranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017. 1 OVERVIEW nmtpy is a refactored, extended and Python 3 only version of dl4mt-tutorial 1, a Theano (Theano Development Team, 2016) implementation of attentive Neural Machine Translation (NMT) (Bahdanau et al., 2014). The development of nmtpy project which has been open-sourced2 under MIT license in March 2017, started in March 2016 as an effort to adapt dl4mt-tutorial to multimodal translation models. nmtpy has now become a powerful toolkit where adding a new model is as simple as deriving from an abstract base class to fill in a set of fundamental methods and (optionally) implementing a custom data iterator. The training and inference utilities are as model-agnostic as possible allowing one to use them for different sequence generation networks such as multimodal NMT and image captioning to name a few. This flexibility and the rich set of provided architectures (Section 3) is what differentiates nmtpy from Nematus (Sennrich et al., 2017) another NMT software derived from dl4mt-tutorial.
NMTPY: A FLEXIBLE TOOLKIT FOR ADVANCED NEURAL MACHINE TRANSLATION SYSTEMS