Large-scale Expected BLEU Training of Phrase-based Reordering Models
Michael Auli, Michel Galley, Jianfeng Gao · 2014
Recent work by Cherry (2013) has shown that directly optimizing phrase-based re-ordering models towards BLEU can lead to significant gains. Their approach is lim-ited to small training sets of a few thou-sand sentences and a similar number of sparse features. We show how the ex-pected BLEU objective allows us to train a simple linear discriminative reordering model with millions of sparse features on hundreds of thousands of sentences re-sulting in significant improvements. A comparison to likelihood training demon-strates that expected BLEU is vastly more effective. Our best results improve a hi-erarchical lexicalized reordering baseline by up to 2.0 BLEU in a single-reference setting on a French-English WMT 2012 setup. 1