Home

nmtpytorch allows training of various end-to-end neural architectures including but not limited to neural machine translation, image captioning and automatic speech recognition systems. The initial codebase was in Theano and was inspired from the famous dl4mt-tutorial codebase.

nmtpytorch is mainly developed by the Language and Speech Team of Le Mans University but receives valuable contributions from the Grounded Sequence-to-sequence Transduction Team of Frederick Jelinek Memorial Summer Workshop 2018:

Loic Barrault, Ozan Caglayan, Amanda Duarte, Desmond Elliott, Spandana Gella, Nils Holzenberger, Chirag Lala, Jasmine (Sun Jae) Lee, Jindřich Libovický, Pranava Madhyastha, Florian Metze, Karl Mulligan, Alissa Ostapenko, Shruti Palaskar, Ramon Sanabria, Lucia Specia and Josiah Wang.

If you use nmtpytorch, you may want to cite the following paper:

@article{nmtpy2017,
  author    = {Ozan Caglayan and
               Mercedes Garc\'{i}a-Mart\'{i}nez and
               Adrien Bardet and
               Walid Aransa and
               Fethi Bougares and
               Lo\"{i}c Barrault},
  title     = {NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems},
  journal   = {Prague Bull. Math. Linguistics},
  volume    = {109},
  pages     = {15--28},
  year      = {2017},
  url       = {https://ufal.mff.cuni.cz/pbml/109/art-caglayan-et-al.pdf},
  doi       = {10.1515/pralin-2017-0035},
  timestamp = {Tue, 12 Sep 2017 10:01:08 +0100}
}