|dc.description.abstract||Phrase-based decoding is conceptually simple and straightforward to
implement, at the cost of drastically oversimplified reordering models.
Syntactically aware models make it possible to capture linguistically
relevant relationships in order to improve word order, but they can be
more complex to implement and optimise.
In this paper, we explore a new middle ground between phrase-based and
syntactically informed statistical MT, in the form of a model that
supplements conventional, non-hierarchical phrase-based techniques with
linguistically informed reordering based on syntactic dependency trees.
The key idea is to exploit linguistically-informed hierarchical
structures only for those dependencies that cannot be captured within a
single flat phrase. For very local dependencies we leverage the success
of conventional phrase-based approaches, which provide a sequence of
target-language words appropriately ordered and ready-made with the
appropriate agreement morphology.
Working with dependency trees rather than constituency trees allows us
to take advantage of the flexibility of phrase-based systems to treat
non-constituent fragments as phrases. We do impose a requirement ---
that the fragment be a novel sort of "dependency constituent" --- on
what can be translated as a phrase, but this is much weaker than the
requirement that phrases be traditional linguistic constituents, which
has often proven too restrictive in MT systems.||en_US
|dc.relation.ispartofseries||UM Computer Science Department;CS-TR-4947||
|dc.title||Extending Phrase-Based Decoding with a Dependency-Based Reordering Model||en_US