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Author: Lucia Specia Publisher: Springer Nature ISBN: 3031021681 Category : Computers Languages : en Pages : 148
Book Description
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.
Author: Lucia Specia Publisher: Springer Nature ISBN: 3031021681 Category : Computers Languages : en Pages : 148
Book Description
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.
Author: Joss Moorkens Publisher: Springer ISBN: 3319912410 Category : Computers Languages : en Pages : 292
Book Description
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.
Author: Ilse Depraetere Publisher: Walter de Gruyter ISBN: 3110259885 Category : Language Arts & Disciplines Languages : en Pages : 285
Book Description
The volume is a collection of papers that deal with the issue of translation quality from a number of perspectives. It addresses the quality of human translation and machine translation, of pragmatic and literary translation, of translations done by students and by professional translators. Quality is not merely looked at from a linguistic point of view, but the wider context of QA in the translation workflow also gets ample attention. The authors take an inductive approach: the papers are based on the analysis of translation data and/or on hands-on experience. The book provides a bird's eye view of the crucial quality issues, the close collaboration between academics and industry professionals safeguarding attention for quality in the 'real world'. For this reason, the methodological stance is likely to inspire the applied researcher. The analyses and descriptions also include best practices for translation trainers, professional translators and project managers.
Author: Sin-wai Chan Publisher: Routledge ISBN: 1351376241 Category : Language Arts & Disciplines Languages : en Pages : 256
Book Description
Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online translation systems. However, despite the rapid advances in machine translation, the role of a human translator remains crucial. As illustrated by the chapters in this book, man-machine interaction is essential in machine translation, localisation, terminology management, and crowdsourcing translation. In fact, the importance of a human translator before, during, and after machine processing, cannot be overemphasised as human intervention is the best way to ensure the translation quality of machine translation. This volume explores the role of a human translator in machine translation from various perspectives, affording a comprehensive look at this topical research area. This book is essential reading for anyone involved in translation studies, machine translation or interested in translation technology.
Author: Huertas-Barros, Elsa Publisher: IGI Global ISBN: 152255226X Category : Language Arts & Disciplines Languages : en Pages : 437
Book Description
The development of translation memories and machine translation have led to new quality assurance practices where translators have found themselves checking not only human translation but also machine translation outputs. As a result, the notions of revision and interpersonal competences have gained great importance with international projects recognizing them as high priorities. Quality Assurance and Assessment Practices in Translation and Interpreting is a critical scholarly resource that serves as a guide to overcoming the challenge of how translation and interpreting results should be observed, given feedback, and assessed. It also informs the design of new ways of evaluating students as well as suggesting criteria for professional quality control. Featuring coverage on a broad range of topics such as quality management, translation tests, and competency-based assessments, this book is geared towards translators, interpreters, linguists, academicians, translation and interpreting researchers, and students seeking current research on the new ways of evaluating students as well as suggesting criteria for professional quality control in translation.
Author: Junhui Li Publisher: Springer Nature ISBN: 981336162X Category : Computers Languages : en Pages : 154
Book Description
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.
Author: Lucia Specia
Author: Lucia Specia
Author: Yu Yuan
Author: Joss Moorkens
Author: Maria Kunilovskaya
Author: Maria Kunilovskaya
Author: Ilse Depraetere
Author: Sin-wai Chan
Author: Huertas-Barros, Elsa
Author: Junhui Li
Author: Juliane House