Machine Translation with Minimal Reliance on Parallel Resources

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.​

George Tambouratzis graduated from the Electrical Engeneering Department of the National Technical University of Athens (1989), and received his M.Sc. (1990) and Ph.D. (1993) degrees from Brunel University. Since 1996 he has been with the Institute for Language and Speech Processing (ILSP), working on machine learning, neural networks and evolutionary computation algorithms for computational linguistics. He is the Director of Research at ILSP and the Head of the Machine Translation Department. He co-ordinated several EU-funded projects.