Linguistically Motivated Statistical Machine Translation: Models and Algorithms [Hardcover]

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.

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

Linguistically Motivated Statistical Machine Translation, written by Deyi Xiong and Min Zhang is an overview of (mostly) already published work by the same researchers, rewritten into a coherent book that explains how several different research aspects fit into one research paradigm. & the book is inspiring and worth reading, if you wish to try out and improve your SMT system. (Vincent Vandeghinste, Machine Translation, Vol. 29, 2015)

Deyi Xiong is a professor at Soochow University. Previously he was a research scientist at the Institute for Infocomm Research of Singapore from 2007-2013. He completed his Ph.D. in Computer Science at the Institute of Computing Technology of Chinese Academy of Sciences in 2007. His research interests are in the area of natural language processing,l³@