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The key assumption in this text is that machine translation is not merely a mechanical process but in fact requires a high level of linguistic sophistication, as the nuances of syntax, semantics and intonation cannot always be conveyed by modern technology. The increasing dependence on artificial communication by private and corporate users makes this research area an invaluable element when teaching linguistic theory.
This book is the first volume that focuses on the specific challenges of machine translation with Arabic either as source or target language. It nicely fills a gap in the literature by covering approaches that belong to the three major paradigms of machine translation: Example-based, statistical and knowledge-based. It provides broad but rigorous coverage of the methods for incorporating linguistic knowledge into empirical MT. The book brings together original and extended contributions from a group of distinguished researchers from both academia and industry. It is a welcome and much-needed repository of important aspects in Arabic Machine Translation such as morphological analysis and syntactic reordering, both central to reducing the distance between Arabic and other languages. Most of the proposed techniques are also applicable to machine translation of Semitic languages other than Arabic, as well as translation of other languages with a complex morphology.
Contrastive Linguistics (CL), Translation Studies (TS) and Machine Translation (MT) have common grounds: They all work at the crossroad where two or more languages meet. Despite their inherent relatedness, methodological exchange between the three disciplines is rare. This special issue touches upon areas where the three fields converge. It results directly from a workshop at the 2011 German Association for Language Technology and Computational Linguistics (GSCL) conference in Hamburg where researchers from the three fields presented and discussed their interdisciplinary work. While the studies contained in this volume draw from a wide variety of objectives and methods, and various areas of overlaps between CL, TS and MT are addressed, the volume is by no means exhaustive with regard to this topic. Further cross-fertilisation is not only desirable, but almost mandatory in order to tackle future tasks and endeavours.}
The use of the computer in translating natural languages ranges from that of a translator's aid for word processing and dictionary lookup to that of a full-fledged translator on its own. However the obstacles to translating by means of the computer are primarily linguistic. To overcome them it is necessary to resolve the ambiguities that pervade a natural language when words and sentences are viewed in isolation. The problem then is to formalize, in the computer, these aspects of natural language understanding. The authors show how, from a linguistic point of view, one may form some idea of what goes on inside a system's black box, given only the input (original text) and the raw output (translated text before post-editing). Many examples of English/French translation are used to illustrate the principles involved.
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.
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program--The Global Autonomous Language Exploitation within the Defense Advanced Research Projects Agency (DARPA), while placing it in the context of previous research in the fields of natural language and signal processing, artificial intelligence and machine translation. The most fundamental contrast between GALE and its predecessor programs was its holistic integration of previously separate or sequential processes. In earlier language research programs, each of the individual processes was performed separately and sequentially: speech recognition, language recognition, transcription, translation, and content summarization. The GALE program employed a distinctly new approach by executing these processes simultaneously. Speech and language recognition algorithms now aid translation and transcription processes and vice versa. This combination of previously distinct processes has produced significant research and performance breakthroughs and has fundamentally changed the natural language processing and machine translation fields. This comprehensive handbook provides an exhaustive exploration into these latest technologies in natural language, speech and signal processing, and machine translation, providing researchers, practitioners and students with an authoritative reference on the topic.
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 book is about the limits of machine translation. It is widely recognized that machine translation systems do much better on domain-specific controlled-language texts (domain texts for short) than on dynamic general-language texts (general texts for short). The authors explore this general domain distinction and come to some uncommon conclusions about the nature of language. Domain language is claimed to be made possible by general language, while general language is claimed to be made possible by the ethical dimensions of relationships. Domain language is unharmed by the constraints of objectivism, while general language is suffocated by those constraints. Along the way to these conclusions, visits are made to Descartes and Saussure, to Chomsky and Lakoff, to Wittgenstein and Levinas. From these conclusions, consequences are drawn for machine translation and translator tools, for linguistic theory and translation theory. The title of the book does not question whether language is possible; it asks, with wonder and awe, why communication through language is possible.