  Pivot language is employed as a way to solve the data sparseness problem in
machine translation, especially when the data for a particular language pair
does not exist. The combination of source-to-pivot and pivot-to-target
translation models can induce a new translation model through the pivot
language. However, the errors in two models may compound as noise, and still,
the combined model may suffer from a serious phrase sparsity problem. In this
paper, we directly employ the word lexical model in IBM models as an additional
resource to augment pivot phrase table. In addition, we also propose a phrase
table pruning method which takes into account both of the source and target
phrasal coverage. Experimental result shows that our pruning method
significantly outperforms the conventional one, which only considers source
side phrasal coverage. Furthermore, by including the entries in the lexicon
model, the phrase coverage increased, and we achieved improved results in
Chinese-to-Japanese translation using English as pivot language.
