To prepare SMT outputs for post-editing , the creators of the corpus used their own WMT10 system ( Potet et al. , 2010 ) , based on the Moses phrase-based decoder ( Koehn et al. , 2007 ) with dense features .
moses open source toolkit for statistical machine translation
Philipp Koehn Hieu Hoang Alexandra Birch Chris Callison-Birch Marcello Federico Nicola Bertoldi Brooke Cowan Wade Shen
The corpus is a subset of the newscommentary dataset provided at WMT4 and contains input French sentences , MT outputs , postedited outputs and English references . To prepare SMT outputs for post-editing , the creators of the corpus used their own WMT10 system ( Potet et al. , 2010 ) , based on the Moses phrase-based decoder ( Koehn et al. , 2007 ) with dense features . We replicated a similar Moses system using the same monolingual and parallel data : a 5-gram language model was estimated with the KenLM toolkit ( Heafield , 2011 ) on news.en data ( 48.65 M sentences , 1.13 B tokens ) , pre-processed with the tools from the cdec toolkit ( Dyer et al. , 2010 ) .
To prepare SMT outputs for post-editing , the creators of the corpus used their own WMT10 system ( Potet et al. , 2010 ) , based on the Moses phrase-based decoder ( @@CITATION ) with dense features .