The minimum Bayes risk (MBR) decoding objective improves BLEU scores for machine translation output relative to the standard Viterbi objective of maximizing model score .

Translation forests compactly encode an exponential number of output translations for an input sentence, along with their model scores .

3Decoder states can include additional information as well, such as local configurations for dependency language model scoring .

The n-gram language model score of e similarly decomposes over the h in e that produce n-grams.

Figure 4: Three translations of an example Arabic sentence: its human- generated reference, the translation with the highest model score under Hiero (Viterbi), and the translation chosen by forest-based consensus decoding.

This paper applies MST parsing to MT, and describes how it can be integrated into a phrase-based decoder to compute dependency language model scores .

While it seems that loopy graphs are undesirable when the goal is to obtain a syntactic analysis, that is not necessarily the case when one just needs a language modeling score .

We use the standard features implemented almost exactly as in Moses: four translation features (phrase-based translation probabilities and lexically-weighted probabilities), word penalty, phrase penalty, linear distortion, and language model score .

model score computed with the dependency parsing algorithm described in Section 2.