Rule-based reordering spaces in statistical machine translation
Abstract
In Statistical Machine Translation (SMT), the constraints on word
reorderings have a great impact on the set of potential translations that are
explored. Notwithstanding computationnal issues, the reordering space
of a SMT system needs to be designed with great care: if a larger
search space is likely to yield better translations, it may also lead
to more decoding errors, because of the added ambiguity and the
interaction with the pruning strategy.
In this paper, we study this trade-off using a state-of-the art
translation system, where all reorderings are represented in a word lattice prior to
decoding. This allows us to directly explore and compare
different reordering spaces. We study in detail a rule-based
preordering system, varying the length or number of rules, the tagset
used, as well as contrasting with oracle settings and purely
combinatorial subsets of permutations. We focus on two language pairs:
English-French, a close language pair and English-German, known to be
a more challenging reordering pair.