This book provides a wide variety of algorithms and models to integrate
linguistic knowledge into Statistical Machine Translation (SMT). It
helps advance conventional SMT to linguistically motivated SMT by
enhancing the following three essential components: translation,
reordering and bracketing models. It also serves the purpose of
promoting the in-depth study of the impacts of linguistic knowledge on
machine translation. Finally it provides a systematic introduction of
Bracketing Transduction Grammar (BTG) based SMT, one of the
state-of-the-art SMT formalisms, as well as a case study of
linguistically motivated SMT on a BTG-based platform.