The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.

This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative, as they elegantly solve many of the shortcomings of phrase-based models. The heart of this book is a detailed introduction to decoding for syntax-based models. The book begins with an overview of synchronous-context free grammar (SCFG) and synchronous tree-substitution grammar (STSG) along with their associated statistical models. It also describes how three popular instantiations (Hiero, SAMT, and GHKM) are learned from parallel corpora. It introduces and details hypergraphs and associated general algorithms, as well as algorithms for decoding with both tree and string input. Special attention is given to efficiency, including search approximations such as beam search and cube pruning, data structures, and parsing algorithms. The book consistently highlights the strengths (and limitations) of syntax-based approaches, including their ability to generalize phrase-based translation units, their modeling of specific linguistic phenomena, and their function of structuring the search space.

Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Verbmobil is the result of eight years of intensive research in a large speech-to-speech translation project, executed by a consortium comprising nineteen academic and four industrial partners. The system that was developed by more than 100 researchers and engineers handles dialogs in three business-oriented domains, with translation between three languages: German, English, and Japanese. Verbmobil deals with spontaneous speech, which includes realistic repair phenomena, and uses deep semantic analysis to recognize a speaker's slips and to translate what he tried to say rather than what he actually said. - This book gives the first comprehensive overview of the results of this unique and seminal project in human language technology. Contributions by leading scientists in speech and language technology look at the component technologies that make Verbmobil the most advanced speech-to-speech translation system worldwide and a landmark project in the history of natural language processing.

How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.

The field of machine translation (MT) - the automation of translation between human languages - has existed for more than 50 years. MT helped to usher in the field of computational linguistics and has influenced methods and applications in knowledge representation, information theory, and mathematical statistics.

Half a centuryago not manypeople had realizedthat a new epoch in the history of homo sapiens had just started. The term “Information Society Age” seems an appropriate name for this epoch. Communication was without a doubt a lever of the conquest of the human race over the rest of the animate world. There is little doubt that the human racebegan when our predecessorsstarted to communicate with each other using language.This highly abstractmeans of communicationwas probably one of the major factors contributing to the evolutionary success of the human race within the animal world. Physically weak and imperfect, humans started to dominate the rest of the world through the creation of communication-based societies where individuals communicated initially to satisfy immediate needs, and then to create, accumulate and process knowledge for future use. The crucial step in the history of humanity was the invention of writing. It is worth noting that writing is a human invention, not a phenomenon resulting from natural evolution. Humans invented writing as a technique for recording speech as well as for storing and facilitating the dissemination of knowledge across the world. Humans continue to be born illiterate, and therefore teaching and conscious supervised learning is necessary to maintain this basic social skill.

This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.