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Readings in Machine Translation by Sergei Nirenburg,H. L. Somers,Yorick Wilks Pdf
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.
Designed for translators and other professional linguists, this work attempts to clarify, explain and exemplify the impact that computers have had and are having on their profession. The book concerns machine translation, computer-aided translation and the future of translation and the computer.
Quality Estimation for Machine Translation by Lucia Specia,Carolina Scarton,Gustavo Henrique Paetzold Pdf
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.
All over the world, people are claiming their rights. Are these claims prompted by similar values and aspirations? And even if human rights are universal, what are the consequences of claiming them in different historical, cultural and material realities? The diversity of African countries considered in this book compels careful thought about these questions.
This is the first book devoted exclusively to knowledge-based machine translation. While most approaches to the machine translation for natural languages seek ways to translate source language texts into target language texts without full understanding of the text, knowledge-based machine translation is based on extracting and representing the meaning of the source text. It is scientifically the most challenging approach to the task of machine translation, and significant progress has been achieved within it in recent years. The authors introduce the general paradigm of knowledge-based MT, survey major recent developments, compare it with other approaches and present a paradigmatic view of its component processes
Extraordinary advances in machine translation over the last three quarters of a century have profoundly affected many aspects of the translation profession. The widespread integration of adaptive “artificially intelligent” technologies has radically changed the way many translators think and work. In turn, groundbreaking empirical research has yielded new perspectives on the cognitive basis of the human translation process. Translation is in the throes of radical transition on both professional and academic levels. The game-changing introduction of neural machine translation engines almost a decade ago accelerated these transitions. This volume takes stock of the depth and breadth of resulting developments, highlighting the emerging rivalry of human and machine intelligence. The gathering and analysis of big data is a common thread that has given access to new insights in widely divergent areas, from literary translation to movie subtitling to consecutive interpreting to development of flexible and powerful new cognitive models of translation.
This book compares and contrasts the principles and practices of rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). Presenting numerous examples, the text introduces language divergence as the fundamental challenge to machine translation, emphasizes and works out word alignment, explores IBM models of machine translation, covers the mathematics of phrase-based SMT, provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT, and analyzes EBMT, showing how translation parts can be extracted and recombined to automatically translate a new input.
Critical Readings in Translation Studies by Mona Baker Pdf
This is an integrated and structured set of progressive readings from translation and related disciplines, which provides a comprehensive overview of the field and how it is developing.
Machine Translation and Translation Theory by Christa Hauenschild,Susanne Heizmann Pdf
The series serves to propagate investigations into language usage, especially with respect to computational support. This includes all forms of text handling activity, not only interlingual translations, but also conversions carried out in response to different communicative tasks. Among the major topics are problems of text transfer and the interplay between human and machine activities.
Machine Translation and Global Research by Lynne Bowker,Jairo Buitrago Ciro Pdf
Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. This book is a must-read for researchers and information professionals eager to maximize the global reach and impact of any form of scholarly work.
Machine Translation with Minimal Reliance on Parallel Resources by George Tambouratzis,Marina Vassiliou,Sokratis Sofianopoulos Pdf
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.
Machine Translation by Bozena Henisz-Dostert,R. R. Macdonald,Michael Zarechnak Pdf
TRENDS IN LINGUISTICS is a series of books that open new perspectives in our understanding of language. The series publishes state-of-the-art work on core areas of linguistics across theoretical frameworks as well as studies that provide new insights by building bridges to neighbouring fields such as neuroscience and cognitive science. TRENDS IN LINGUISTICS considers itself a forum for cutting-edge research based on solid empirical data on language in its various manifestations, including sign languages. It regards linguistic variation in its synchronic and diachronic dimensions as well as in its social contexts as important sources of insight for a better understanding of the design of linguistic systems and the ecology and evolution of language. TRENDS IN LINGUISTICS publishes monographs and outstanding dissertations as well as edited volumes, which provide the opportunity to address controversial topics from different empirical and theoretical viewpoints. High quality standards are ensured through anonymous reviewing.
Syntax-based Statistical Machine Translation by Philip Williams,Rico Sennrich,Matt Post Pdf
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.
