Machine learning in translation corpora processing
Author(s)
Bibliographic Information
Machine learning in translation corpora processing
(A Science Publishers book)
CRC Press, c2019
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.
Table of Contents
Table of contents
Preface
Introduction
Background and context
Machine translation (MT)
Statistical machine translation and comparable corpora
Overview of SMT
Textual Components and Corpora
Moses Tool Environment For SMT
Aspects of SMT processing
Evaluation of SMT Quality
State of the Art
Current methods and results in spoken language translation
Recent methods in comparable corpora exploration
Author's solutions to PL-EN corpora processing problems
Parallel data mining improvements
Multi-threaded, Tuned and GPU-accelerated Yalign
Tuning of Yalign method
Minor improvements in mining for Wikipedia exploration
Parallel data mining using other methods
SMT Metric Enhancements
Alignment and filtering of corpora
Baseline system training
Description of experiments
Results and conclusions
Machine translation results
Evaluation of obtained comparable corpora
Quasi comparable corpora exploration
Other fields of MT techniques application
Final conclusions
References
by "Nielsen BookData"