Recent Advances in Example-Based Machine Translation is of relevance to researchers and program developers in the field of Machine Translation and especially Example-Based Machine Translation, bilingual text processing and cross-linguistic information retrieval. It is also of interest to translation technologists and localisation professionals. Recent Advances in Example-Based Machine Translation fills a void, because it is the first book to tackle the issue of EBMT in depth. It gives a state-of-the-art overview of EBMT techniques and provides a coherent structure in which all aspects of EBMT are embedded. Its contributions are written by long-standing researchers in the field of MT in general, and EBMT in particular. This book can be used in graduate-level courses in machine translation and statistical NLP.

Seminar paper from the year 2007 in the subject English Language and Literature Studies - Linguistics, grade: 2, University of Marburg (Fremdsprachliche Philologien), course: Human Language Technologies, language: English, abstract: Machine Translation has more and more become an essential method to assist or even replace human translators. The necessity of developing useful computer software that fulfils this task has grown because in the age of the internet people want to get their information in their own language. Which approach is appropriate and which technique works well in order to cope with this challenge? This paper will focus on Example-based Machine Translation (EBMT), an approach that does not correspond with traditional translation systems but has the advantage of requiring only little knowledge and thus being usable in a great number of languages.

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

This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.​

A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools—computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.

In 1975, Searle stated that one should speak idiomatically unless there is some good reason not to do so. Fillmore, Kay, and O’Connor in 1988 defined an idiomatic expression or construction as something that a language user could fail to know while knowing everything else in the language. Our language is rich in conversational phrases, idioms, metaphors, and general expressions used in metaphorical meaning. These idiomatic expressions pose a particular challenge for Machine Translation (MT), because their translation for the most part does not work literally, but logically. The present book shows how idiomatic expressions can be recognized and correctly translated with the help of a bilingual idiom dictionary (English-German), a monolingual (German) corpus, and morphosyntactic rules. The work focuses on the field of Example-based Machine Translation (EBMT). A theory of idiomatic expressions with their syntactic and semantic properties is provided, followed by the practical part of the book which describes how the hybrid EBMT system METIS-II is able to correctly process idiomatic expressions. A comparison of METIS-II with three commercial systems shows that idioms are not impossible to translate as it was predicted in 1952: “The only way for a machine to treat idioms is—not to have idioms!” This book furnishes plenty of examples of idiomatic phrases and provides the foundation for how MT systems can process and translate idioms by means of simple linguistic resources.