Machine Translation

"The proposed project on machine translation will be based on the above pedagogy, through the study of phenomena, formalization, and then elucidation of the techniques. Case studies, examples, and historical perspectives will be used extensively to cover the material. The primary aim of this book is to provide an accessible text book on machine translation covering lucidly the foundations, insights, and case studies for practical concerns. The book would also point towards where the field is currently and heading towards in the future"-- This book discusses the three major paradigms of machine translation: rule-based, statistical, and example-based, and provides examples and insight-generating exercises..'-- Read more... Abstract: "The proposed project on machine translation will be based on the above pedagogy, through the study of phenomena, formalization, and then elucidation of the techniques. Case studies, examples, and historical perspectives will be used extensively to cover the material. The primary aim of this book is to provide an accessible text book on machine translation covering lucidly the foundations, insights, and case studies for practical concerns. The book would also point towards where the field is currently and heading towards in the future"-- This book discusses the three major paradigms of machine translation: rule-based, statistical, and example-based, and provides examples and insight-generating exercises..' List of FiguresList of TablesPrefaceAcknowledgmentsAbout the AuthorIntroductionA Feel for a Modern Approach to Machine Translation: Data-Driven MTMT Approaches: Vauquois TriangleUnderstanding Transfer over the Vauquois TriangleUnderstanding Ascending and Descending TransferLanguage Divergence with Illustration between Hindi and EnglishSyntactic DivergenceLexical-Semantic DivergenceThree Major Paradigms of Machine TranslationMT EvaluationAdequacy and FluencyAutomatic Evaluation of MT OutputSummaryFurther ReadingLearning Bilingual Word MappingsA Combinatorial ArgumentNecessary and Sufficient Conditions for Deterministic Alignment in Case of One-to-One Word MappingA Naïve Estimate for Corpora RequirementDeeper Look at One-to-One AlignmentDrawing Parallels with Part of Speech TaggingHeuristics-Based Computation of the VE VF TableIterative (EM-Based) Computation of the VE VF TableInitialization and Iteration 1 of EMIteration 2Iteration 3Mathematics of AlignmentA Few Illustrative Problems to Clarify Application of EMDerivation of Alignment ProbabilitiesExpressing the E- and M-Steps in Count FormComplexity ConsiderationsStorageTimeEM: Study of Progress in Parameter ValuesNecessity of at Least Two SentencesOne-Same-Rest-Changed SituationOne-Changed-Rest-Same SituationSummaryFurther ReadingIBM Model of AlignmentFactors Influencing P(f 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 MTanalysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combinationdata 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, the 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. 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.