The work I am going to present is based on the observation that the
two prevailing paradigms in Machine Translation (MT) exhibit
complementary strengths and weaknesses: Statistical MT (SMT) systems
are robust and quick to develop but often fail to deliver correct
translations for syntactically or semantically more complex input.
Rule-based MT (RBMT) systems, on the other hand, are capable of
covering linguistically difficult phenomena but generally suffer from
decreased robustness and the development bottleneck of hand-crafting
transfer rules.

In this talk, I will present a hybrid approach combining 'deep' RBMT
based on semantic transfer with SMT-like data-driven rule acquisition,
thus integrating both worlds' major advantages. The method involves
parsing a sentence-aligned bilingual corpus with Head-Driven Prase
Structure Grammars (HPSG) and the extraction of transfer rules by
aligning the resulting semantic representations.