This work proposes to adapt an existing
general SMT model for the task of translating
queries that are subsequently going to
be used to retrieve information from a target
language collection. In the scenario that
we focus on access to the document collection
itself is not available and changes to
the IR model are not possible. We propose
two ways to achieve the adaptation effect
and both of them are aimed at tuning parameter
weights on a set of parallel queries.
The first approach is via a standard tuning
procedure optimizing for BLEU score and
the second one is via a reranking approach
optimizing for MAP score. We also extend
the second approach by using syntax-based
features. Our experiments show improvements
of 1-2.5 in terms of MAP score over
the retrieval with the non-adapted translation.
We show that these improvements are
due both to the integration of the adaptation
and syntax-features for the query translation
task.