Machine Translation with Minimal Reliance on Parallel Resources

  • George Tambouratzis
  • Marina Vassiliou
  • Sokratis Sofianopoulos

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-ix
  2. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 1-10
  3. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 11-28
  4. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 29-41
  5. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 43-53
  6. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 55-61
  7. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 63-75
  8. George Tambouratzis, Marina Vassiliou, Sokratis Sofianopoulos
    Pages 77-85
  9. Back Matter
    Pages 87-88

About this book

Introduction

This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.​

Keywords

MT portability to new language pairs Machine learning methods Machine translation (MT) Non-reliance on expensive resources Statistical methods for language processing Pattern recognition Applied linguistics Natural language processing

Authors and affiliations

  • George Tambouratzis
    • 1
  • Marina Vassiliou
    • 2
  • Sokratis Sofianopoulos
    • 3
  1. 1.Institute for Language and Speech ProcessingAthensGreece
  2. 2.Institute for Language and Speech ProcessingAthensGreece
  3. 3.Institute for Language and Speech ProcessingAthensGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-63107-3
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-63105-9
  • Online ISBN 978-3-319-63107-3
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • About this book