Machine Translation with Minimal Reliance on Parallel Resources [Paperback]

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.

Chapter 1: Preliminaries 1

1.1 Challenges in MT  Relevance to the European environment 1

1.2 A brief review of MT development history. 2

1.3 Advantages and disadvantages of main MT paradigms 3

1.4 The PRESEMT methodology in a nutshell 7

1.5 Closing note on implementation. 9

1.6 References 9

1.7 Glossary of Terms 12

Chapter 2: Implementation. 14

2.1 Introduction: Summary of the approach. 14

2.2 Linguistic resources: Data and existing linguistic tools 15

2.2.1????? External processing tools 16

2.2.2????? Lemma-based bilingual dictionary. 17

2.2.3????? The parallel corpus 19

2.2.4????? The TL monolingual corpus 22

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