Neural sequence to sequence learning recently became a very promising paradigm in machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.
@article{arxiv.1606.07481,
title = {CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks},
author = {Jindřich Libovický and Jindřich Helcl and Marek Tlustý and Pavel Pecina and Ondřej Bojar},
journal= {arXiv preprint arXiv:1606.07481},
year = {2016}
}
Comments
Accepted to the First Conference of Machine Translation (WMT16)