Description
This book compares and contrasts the principles and practices of rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). Presenting numerous examples, the text introduces language divergence as the fundamental challenge to machine translation, emphasizes and works out word alignment, explores IBM models of machine translation, covers the mathematics of phrase-based SMT, provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT, and analyzes EBMT, showing how translation parts can be extracted and recombined to automatically translate a new input.
Table of Contents
List of Figures, List of Tables, Preface, Acknowledgments, About the Author, 1. Introduction, 2. Learning Bilingual Word Mappings, 3. IBM Model of Alignment, 4. Phrase-Based Machine Translation, 5. Rule-Based Machine Translation (RBMT), 6. Example-Based Machine Translation, Index
