Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text:

• Introduces MT against the backdrop of language divergence and the Vauquois triangle
• Presents expectation maximization (EM)-based word alignment as a turning point in the history of MT
• Discusses the most important element of SMT-bilingual word alignment from pairs of parallel translations
• Explores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentence
• Covers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environment
• Provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT
• Analyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automatically
• Includes numerous examples that illustrate universal translation phenomena through the usage of specific languages

Machine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers.

Print Versions of this book also include access to the ebook version.