Publication at Faculty of Mathematics and Physics |
2017
Abstract
This paper describes the neural and phrase-based machine translation systems submitted by CUNI to English-Czech News Translation Task of WMT17. We experiment with synthetic data for training and try several system combination techniques, both neural and phrase-based.
Our primary submission CU-CHIMERA ends up being phrase-based backbone which incorporates neural and deep-syntactic candidate translations.