Publication at Faculty of Mathematics and Physics |
2015
Abstract
We present a feature-rich discriminative model for machine translation which uses an abstract semantic representation on the source side. We include our model as an additional feature in a phrase-based decoder and we show modest gains in BLEU score in an n-best re-ranking experiment.