Download or read book Quality Estimation for Machine Translation written by Lucia Specia and published by Springer Nature. This book was released on 2022-05-31 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Comparative Quality Estimation for Machine Translation written by Eleftherios Avramidis and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Post editing and Quality Estimation for Machine Translation Systems written by Hanna Bechara and published by . This book was released on 2014 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical post-editing (SPE) has been successfully applied to RBMT systems and, to a less successful extent, to some SMT systems. This thesis investigates the impact of SPE on SMT systems. We apply SPE to an SMT system using a new context-modelling approach to preserve some aspects of source information in the second stage translation. This technique yields mixed results, but fails to consistently improve the output over the baseline. Furthermore, we compared the results to those of an RBMT+SPE system and a pure SMT system, using both automatic and human evaluation methods. Results show that while automatic evaluation metrics favour a pure SMT system, manual evaluators prefer the output provided by the combined RBMT+SPE system. We investigate the use machine learning methods to predict which sentences would benefit from post-editing, however, as the oracle score for both SMT and SMT+SPE was not much higher than the two systems alone, we decided to compare two systems that had a higher upper bound. Combining our analysis with machine learning techniques for quality estimation, we are able to improve the overall output by automatically selecting the best sentences from each of the SMT and RBMT+SPE systems.
Download or read book Translation Quality Assessment written by Joss Moorkens and published by Springer. This book was released on 2018-07-13 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Document level Machine Translation Quality Estimation written by Carolina Scarton and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Translation written by Junhui Li and published by Springer Nature. This book was released on 2021-01-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Statistical Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2010 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Investigating Continuous Space Language Models for Machine Translation Quality Estimation written by Kashif Shah and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Translation Systems written by Jonathan Slocum and published by . This book was released on 1988 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Download or read book Post editing of Machine Translation written by Laura Winther Balling and published by Cambridge Scholars Publishing. This book was released on 2014-03-17 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Post-editing is possibly the oldest form of human-machine cooperation for translation. It has been a common practice for just about as long as operational machine translation systems have existed. Recently, however, there has been a surge of interest in post-editing among the wider user community, partly due to the increasing quality of machine translation output, but also to the availability of free, reliable software for both machine translation and post-editing. As a result, the practices and processes of the translation industry are changing in fundamental ways. This volume is a compilation of work by researchers, developers and practitioners of post-editing, presented at two recent events on post-editing: The first Workshop on Post-editing Technology and Practice, held in conjunction with the 10th Conference of the Association for Machine Translation in the Americas, held in San Diego, in 2012; and the International Workshop on Expertise in Translation and Post-editing Research and Application, held at the Copenhagen Business School, in 2012.
Download or read book Word Confidence Estimation and Its Applications in Statistical Machine Translation written by Ngoc Quang Luong and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Translation (MT) systems, which generate automatically the translation of a target language for each source sentence, have achieved impressive gains during the recent decades and are now becoming the effective language assistances for the entire community in a globalized world. Nonetheless, due to various factors, MT quality is still not perfect in general, and the end users therefore expect to know how much should they trust a specific translation. Building a method that is capable of pointing out the correct parts, detecting the translation errors and concluding the overall quality of each MT hypothesis is definitely beneficial for not only the end users, but also for the translators, post-editors, and MT systems themselves. Such method is widely known under the name Confidence Estimation (CE) or Quality Estimation (QE). The motivations of building such automatic estimation methods originate from the actual drawbacks of assessing manually the MT quality: this task is time consuming, effort costly, and sometimes impossible in case where the readers have little or no knowledge of the source language. This thesis mostly focuses on the CE methods at word level (WCE). The WCE classifier tags each word in the MT output a quality label. The WCE working mechanism is straightforward: a classifier trained beforehand by a number of features using ML methods computes the confidence score of each label for each MT output word, then tag this word with highest score label. Nowadays, WCE shows an increasing importance in many aspects of MT. Firstly, it assists the post-editors to quickly identify the translation errors, hence improve their productivity. Secondly, it informs readers of portions of sentence that are not reliable to avoid the misunderstanding about the sentence's content. Thirdly, it selects the best translation among options from multiple MT systems. Last but not least, WCE scores can help to improve the MT quality via some scenarios: N-best list re-ranking, Search Graph Re-decoding, etc. In this thesis, we aim at building and optimizing our baseline WCE system, then exploiting it to improve MT and Sentence Confidence Estimation (SCE). Compare to the previous approaches, our novel contributions spread of these following main points. Firstly, we integrate various types of prediction indicators: system-based features extracted from the MT system, together with lexical, syntactic and semantic features to build the baseline WCE systems. We also apply multiple Machine Learning (ML) models on the entire feature set and then compare their performances to select the optimal one to optimize. Secondly, the usefulness of all features is deeper investigated using a greedy feature selection algorithm. Thirdly, we propose a solution that exploits Boosting algorithm as a learning method in order to strengthen the contribution of dominant feature subsets to the system, thus improve of the system's prediction capability. Lastly, we explore the contributions of WCE in improving MT quality via some scenarios. In N-best list re-ranking, we synthesize scores from WCE outputs and integrate them with decoder scores to calculate again the objective function value, then to re-order the N-best list to choose a better candidate. In the decoder's search graph re-decoding, the proposition is to apply WCE score directly to the nodes containing each word to update its cost regarding on the word quality. Furthermore, WCE scores are used to build useful features, which can enhance the performance of the Sentence Confidence Estimation system. In total, our work brings the insightful and multidimensional picture of word quality prediction and its positive impact on various sectors for Machine Translation. The promising results open up a big avenue where WCE can play its role, such as WCE for Automatic Speech Recognition (ASR) System, WCE for multiple MT selection, and WCE for re-trainable and self-learning MT systems.
Download or read book Exploring Prediction Uncertainty in Machine Translation Quality Estimation written by Daniel Beck and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Human Factor in Machine Translation written by Sin-wai Chan and published by Routledge. This book was released on 2018-05-08 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Recent Trends in Computational Intelligence written by Ali Sadollah and published by BoD – Books on Demand. This book was released on 2020-05-06 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications.
Download or read book Machine Translation written by Shujian Huang and published by Springer Nature. This book was released on 2019-11-22 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th China Conference on Machine Translation, CCMT 2019, held in Nanchang, China, in September 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 21 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
Download or read book Progress in Machine Translation written by Sergei Nirenburg and published by IOS Press. This book was released on 1993 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt:
