Using Dependency Order Templates to Improve Generality in Translation

Today’s statistical machine translation
systems generalize poorly to new
domains. Even small shifts can cause
precipitous drops in translation quality.
Phrasal systems rely heavily, for both
reordering and contextual translation, on
long phrases that simply fail to match outof-
domain text. Hierarchical systems
attempt to generalize these phrases but
their learned rules are subject to severe
constraints. Syntactic systems can learn
lexicalized and unlexicalized rules, but the
joint modeling of lexical choice and
reordering can narrow the applicability of
learned rules. The treelet approach models
reordering separately from lexical choice,
using a discriminatively trained order
model, which allows treelets to apply
broadly, and has shown better
generalization to new domains, but suffers
a factorially large search space. We
introduce a new reordering model based
on dependency order templates, and show
that it outperforms both phrasal and treelet
systems on in-domain and out-of-domain
text, while limiting the search space.