Description
T and F CRC Machine Translation by Bhattacharyya and P
Three paradigms have dominated machine translation (MT)?rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT?analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination?data supplies translation parts that rules recombine to produce translation. 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. 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.
Key Features:-
- 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 translat
- Explores the IBM models of MT, explaining how to find the best alignment given a translation pair an
- 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