This book constitutes the refereed proceedings of the 16th China Conference on Machine Translation, CCMT 2020, held in Hohhot, China, in October 2020. The 13 papers presented in this volume were carefully reviewed and selected from 78 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.

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.

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

This is the first volume that brings together research and practice from academic and industry settings and a combination of human and machine translation evaluation. Its comprehensive collection of papers by leading experts in human and machine translation quality and evaluation who situate current developments and chart future trends fills a clear gap in the literature. This is critical to the successful integration of translation technologies in the industry today, where the lines between human and machine are becoming increasingly blurred by technology: this affects the whole translation landscape, from students and trainers to project managers and professionals, including in-house and freelance translators, as well as, of course, translation scholars and researchers. The editors have broad experience in translation quality evaluation research, including investigations into professional practice with qualitative and quantitative studies, and the contributors are leading experts in their respective fields, providing a unique set of complementary perspectives on human and machine translation quality and evaluation, combining theoretical and applied approaches.

The Routledge Encyclopedia of Translation Technology provides a state-of-the art survey of the field of computer-assisted translation. It is the first definitive reference to provide a comprehensive overview of the general, regional and topical aspects of this increasingly significant area of study. The Encyclopedia is divided into three parts: Part One presents general issues in translation technology, such as its history and development, translator training and various aspects of machine translation, including a valuable case study of its teaching at a major university; Part Two discusses national and regional developments in translation technology, offering contributions covering the crucial territories of China, Canada, France, Hong Kong, Japan, South Africa, Taiwan, the Netherlands and Belgium, the United Kingdom and the United States Part Three evaluates specific matters in translation technology, with entries focused on subjects such as alignment, bitext, computational lexicography, corpus, editing, online translation, subtitling and technology and translation management systems. The Routledge Encyclopedia of Translation Technology draws on the expertise of over fifty contributors from around the world and an international panel of consultant editors to provide a selection of articles on the most pertinent topics in the discipline. All the articles are self-contained, extensively cross-referenced, and include useful and up-to-date references and information for further reading. It will be an invaluable reference work for anyone with a professional or academic interest in the subject.

Machine Translation (MT) has become widely used throughout the world as a medium of communication between those who live in different countries and speak different languages. However, translation between distant languages constitutes a challenge for machines. Therefore, translation evaluation is poised to play a significant role in the process of designing and developing effective MT systems. This book evaluates three prominent MT systems, including Google Translate, Microsoft Translator, and Sakhr, each of which provides translation between English and Arabic. In the book Almahasees scrutinizes the capacity of the three systems in dealing with translation between English and Arabic in a large corpus taken from various domains, including the United Nation (UN), the World Health Organization (WHO), the Arab League, Petra News Agency reports, and two literary texts: The Old Man and the Sea and The Prophet. The evaluation covers holistic analysis to assess the output of the three systems in terms of Translation Automation User Society (TAUS) adequacy and fluency scales. The text also looks at error analysis to evaluate the systems’ output in terms of orthography, lexis, grammar, and semantics at the entire-text level and in terms of lexis, grammar, and semantics at the collocation level. The research findings contained within this volume provide important feedback about the capabilities of the three MT systems with respect to EnglishArabic translation and paves the way for further research on such an important topic. This book will be of interest to scholars and students of translation studies and translation technology.

This book assembles fifteen original, interdisciplinary research chapters that explore methodological and conceptual considerations as well as user and usage studies to elucidate the relation between the translation product and translation/post-editing processes. It introduces numerous innovative empirical/data-driven measures as well as novel classification schemes and taxonomies to investigate and quantify the relation between translation quality and translation effort in from-scratch translation, machine translation post-editing and computer-assisted audiovisual translation. The volume addresses questions in the translation of cognates, neologisms, metaphors, and idioms, as well as figurative and cultural specific expressions. It re-assesses the notion of translation universals and translation literality, elaborates on the definition of translation units and syntactic equivalence, and investigates the impact of translation ambiguity and translation entropy. The results and findings are interpreted in the context of psycho-linguistic models of bilingualism and re-frame empirical translation process research within the context of modern dynamic cognitive theories of the mind. The volume bridges the gap between translation process research and machine translation research. It appeals to students and researchers in the fields.