Kawaguchi-shi

In Corpus-Based Machine Translation, the search space of the translation candidates for a given input sentence is often defined by a set of (cycle-free) context-free grammar rules. This happens naturally in Syntax-Based Machine Translation and Hierarchi-cal Phrase-Based Machine Translation (where the representation will be the set of the target-side half of the syn-chronous rules used to parse the input sentence). But it is also possible to describe Phrase-Based Machine Trans-lation in this framework. We propose a natural extension to this representa-tion by using lattice-rules that allow to easily encode an exponential num-ber of variations of each rules. We also demonstrate how the representation of the search space has an impact on de-coding efficiency, and how it is possible to optimize this representation.