264 Pages 46 B/W Illustrations
    by Chapman & Hall

    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, 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.

    List of Figures

    List of Tables

    Preface

    Acknowledgments

    About the Author

    Introduction

    A Feel for a Modern Approach to Machine Translation: Data-Driven MT

    MT Approaches: Vauquois Triangle

    Understanding Transfer over the Vauquois Triangle

    Understanding Ascending and Descending Transfer

    Language Divergence with Illustration between Hindi and English

    Syntactic Divergence

    Lexical-Semantic Divergence

    Three Major Paradigms of Machine Translation

    MT Evaluation

    Adequacy and Fluency

    Automatic Evaluation of MT Output

    Summary

    Further Reading

    Learning Bilingual Word Mappings

    A Combinatorial Argument

    Necessary and Sufficient Conditions for Deterministic Alignment in Case of One-to-One Word Mapping

    A Naïve Estimate for Corpora Requirement

    Deeper Look at One-to-One Alignment

    Drawing Parallels with Part of Speech Tagging

    Heuristics-Based Computation of the VE × VF Table

    Iterative (EM-Based) Computation of the VE × VF Table

    Initialization and Iteration 1 of EM

    Iteration 2

    Iteration 3

    Mathematics of Alignment

    A Few Illustrative Problems to Clarify Application of EM

    Derivation of Alignment Probabilities

    Expressing the E- and M-Steps in Count Form

    Complexity Considerations

    Storage

    Time

    EM: Study of Progress in Parameter Values

    Necessity of at Least Two Sentences

    One-Same-Rest-Changed Situation

    One-Changed-Rest-Same Situation

    Summary

    Further Reading

    IBM Model of Alignment

    Factors Influencing P(f|e)

    Alignment Factor a

    Length Factor m

    IBM Model 1

    The Problem of Summation over Product in IBM Model 1

    EM for Computing P(f|e)

    Alignment in a New Input Sentence Pair

    Translating a New Sentence in IBM Model 1: Decoding

    IBM Model 2

    EM for Computing P(f|e) in IBM Model 2

    Justification for and Linguistic Viability of P(i|j,l,m)

    IBM Model 3

    Summary

    Further Reading

    Phrase-Based Machine Translation

    Need for Phrase Alignment

    Case of Promotional/Demotional Divergence

    Case of Multiword (Includes Idioms)

    Phrases Are Not Necessarily Linguistic Phrases

    An Example to Illustrate Phrase Alignment Technique

    Two-Way Alignments

    Symmetrization

    Expansion of Aligned Words to Phrases

    Phrase Table

    Mathematics of Phrase-Based SMT

    Understanding Phrase-Based Translation through an Example

    Deriving Translation Model and Calculating Translation and Distortion Probabilities

    Giving Different Weights to Model Parameters

    Fixing λ Values: Tuning

    Decoding

    Example to Illustrate Decoding

    Moses

    Installing Moses

    Workflow for Building a Phrase-Based SMT System

    Preprocessing for Moses

    Training Language Model

    Training Phrase Model

    Tuning

    Decoding Test Data

    Evaluation Metric

    More on Moses

    Summary

    Further Reading

    Rule-Based Machine Translation (RBMT)

    Two Kinds of RBMT: Interlingua and Transfer

    What Exactly Is Interlingua?

    Illustration of Different Levels of Transfer

    Universal Networking Language (UNL)

    Illustration of UNL

    UNL Expressions as Binary Predicates

    Why UNL?

    Interlingua and Word Knowledge

    How Universal Are UWs?

    UWs and Multilinguality

    UWs and Multiwords

    UW Dictionary and Wordnet

    Comparing and Contrasting UW Dictionary and Wordnet

    Translation Using Interlingua

    Illustration of Analysis and Generation

    Details of English-to-UNL Conversion: With Illustration

    Illustrated UNL Generation

    UNL-to-Hindi Conversion: With Illustration

    Function Word Insertion

    Case Identification and Morphology Generation

    Representative Rules for Function Words Insertion

    Syntax Planning

    Transfer-Based MT

    What Exactly Are Transfer Rules?

    Case Study of Marathi-Hindi Transfer-Based MT

    Krudant: The Crux of the Matter in M-H MT

    M-H MT System

    Summary

    Further Reading

    Example-Based Machine Translation

    Illustration of Essential Steps of EBMT

    Deeper Look at EBMT’s Working

    Word Matching

    Matching of Have

    EBMT and Case-Based Reasoning

    Text Similarity Computation

    Word Based Similarity

    Tree and Graph Based Similarity

    CBR’s Similarity Computation Adapted to EBMT

    Recombination: Adaptation on Retrieved Examples

    Based on Sentence Parts

    Based on Properties of Sentence Parts

    Recombination Using Parts of Semantic Graph

    EBMT and Translation Memory

    EBMT and SMT

    Summary

    Further Reading

    Index

    Biography

    Pushpak Bhattacharyya is Vijay and Sita Vashee chair professor of computer science and engineering at the Indian Institute of Technology (IIT) Bombay, where he has been teaching and researching for the last 25 years. He was educated at IIT Kharagpur (B.Tech), IIT Kanpur (M.Tech), and IIT Bombay (Ph.D). While earning his Ph.D, he was visiting scholar at the Massachusetts Institute of Technology. Subsequently, he has been visiting professor at Stanford University and University of Grenoble, and distinguished lecturer at the University of Houston. Dr. Bhattacharyya’s research interests include natural language processing, machine learning, machine translation, information extraction, sentiment analysis, and cross-lingual search, in which he has published extensively. Currently, he is associate editor of ACM Transactions on Asian Language Information Processing and vice president-elect of Association of Computational Linguistics (ACL).

    "…a clear, well-written introduction to a key area in computer science."
    —Ernest Davis, in Computing Reviews