A simple statistical machine translation method, word-by-word decoding, where not a parallel corpus but a bilingual lexicon is necessary, is adopted for the treebank translation.

A simple statistical machine translation technique, word-by-word decoding, where only a bilingual lexicon is necessary, is used to translate the source treebank.

In addition, a standard statistical machine translation method based on a parallel corpus will not work effectively if it is not able to find a parallel corpus that right covers source and target treebanks.

The second is that a parallel corpus is required for their work and a strict statistical machine translation procedure was performed, while our approach holds a merit of simplicity as only a bilingual lexicon is required.

A word-by-word statistical machine translation strategy is adopted to translate words attached with the respective dependency information from the source language to the target one.

Statistical machine translation (SMT) models require bilingual corpora for training, and these corpora are often multilingual with parallel text in multiple languages simultaneously.

The main source of training data for statistical machine translation (SMT) models is a parallel corpus.

11 Statistical Machine Translation*

Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of—the-art Statistical Machine Translation (SMT) systems.

Statistical Machine Translation (SMT) systems have improved considerably by directly using the error criterion in both training and decoding.

In the context of statistical machine translation , the optimization procedure was first described in Och (2003) for N -best lists and later extended to phrase-lattices in Macherey et al.