Rule Selection with Soft Syntactic Features for String-to-Tree Statistical Machine Translation

Fabienne Braune 
Nina Seemann 
Alexander Fraser

EMNLP 2015

In syntax-based machine translation, rule selection is the task of
choosing the correct target side of a translation rule among rules
with the same source side. We define a discriminative rule selection
model for systems that have syntactic annotation on the target
language side (stringto-tree). This is a new and clean way to
integrate soft source syntactic constraints into string-to-tree
systems as features of the rule selection model. We release our
implementation as part of Moses.