Statistical Machine Translation

Download or read book Statistical Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2010 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Syntax-based Statistical Machine Translation written by Philip Williams and published by Springer Nature. This book was released on 2022-05-31 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Download or read book Verbmobil: Foundations of Speech-to-Speech Translation written by Wolfgang Wahlster and published by Springer Science & Business Media. This book was released on 2000-07-31 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Readings in Machine Translation written by Sergei Nirenburg and published by MIT Press. This book was released on 2003 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Learning Machine Translation written by Cyril Goutte and published by MIT Press. This book was released on 2009 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.

Download or read book Quality Estimation for Machine Translation written by Lucia Specia and published by Springer Nature. This book was released on 2022-05-31 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used in production (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications, including text simplification, text summarization, grammatical error correction, and natural language generation.