Hybrid Approaches to Machine Translation

This volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures.

The book is of interest primarily to MT specialists, but also – in the wider fields of Computational Linguistics, Machine Learning and Data Mining – to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.

Preface.- Foreword.- Chapter 1. Hybrid Machine Translation Overview.- Part 1: Adding Linguistics into SMT.- Chapter 2.  Controllent Ascent: Imbuing Statistical MT with Linguistic knowledge.- Chapter 3.  Hybrid Word Alignment.- Chapter 4.  Syntax in SMT.- Part 2. Using Machine Learning in MT.- Chapter 5.  Machine Learning in RBMT.- Chapter 6. Language-Independent Hybrid MT.- Part 3.  Hybrid NLP tools useful for MT.- Chapter 7. Use of Dependency Parsers in MT.- Chapter 8.  Word Sense Disambiguation in MT.