A Joint Rule Selection Model for Hierarchical Phrase-Based Translation

In hierarchical phrase-based SMT systems,
statistical models are integrated to
guide the hierarchical rule selection for
better translation performance. Previous
work mainly focused on the selection of
either the source side of a hierarchical rule
or the target side of a hierarchical rule
rather than considering both of them simultaneously.
This paper presents a joint
model to predict the selection of hierarchical
rules. The proposed model is estimated
based on four sub-models where the
rich context knowledge from both source
and target sides is leveraged. Our method
can be easily incorporated into the practical
SMT systems with the log-linear
model framework. The experimental results
show that our method can yield significant
improvements in performance.