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.
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