Full Description
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.
Contents
1 Introduction.- 2 BTG-Based SMT.- 3 Syntactically Annotated Reordering.- 4 Semantically Informed Reordering.- 5 Lexicalized Bracketing.- 6 Linguistically Motivated Bracketing.- 7 Translation Rule Selection with Document-Level Semantic Information.- 8 Translation Error Detection with Linguistic Features.- 9 Closing Remarks.- Index.- References.
