Some Statistical Machine Translation systems never see the light because the owner of the appropriate training data cannot release them, and the potential user of the system cannot disclose what should be translated.

It is generally taken for granted that whoever is deploying a Statistical Machine Translation (SMT) system has unrestricted rights to access and use the parallel data required for its training.

private access to a phrase table or other resources for the purpose of performing statistical machine translation .

Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific purpose.

The ability to predict the amount of parallel data required to achieve a given level of quality is very valuable in planning business deployments of statistical machine translation ; yet, we are not aware of any rigorous proposal for addressing this need.

Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific business purpose.

In this paper we show how to train statistical machine translation systems on real-life tasks using only nonparallel monolingual data from two languages.

We presented a method for learning statistical machine translation models from nonparallel data.

This work serves as a big step towards large-scale unsupervised training for statistical machine translation systems.

Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain.

Statistical machine translation (SMT) systems require large parallel corpora in order to be able to obtain a reasonable translation quality.

els in Statistical Machine Translation

Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage.

In this paper, we propose two discriminative, feature-based models to exploit predicate-argument structures for statistical machine translation : 1) a predicate translation model and 2) an argument reordering model.

Recent years have witnessed increasing efforts towards integrating predicate-argument structures into statistical machine translation (SMT) (Wu and Fung, 2009b; Liu and Gildea, 2010).