Download or read book Large scale Multitask Learning for Machine Translation Quality Estimation written by Kashif Shah 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 Online Multitask Learning for Machine Translation Quality Estimation written by José G. C. de Souza 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 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 Machine Translation written by Jinsong Su and published by Springer Nature. This book was released on 2021-10-29 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th China Conference on Machine Translation, CCMT 2020, held in Xining, China, in October 2021. The 10 papers presented in this volume were carefully reviewed and selected from 25 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 Neural Machine Translation for Multimodal Interaction written by Koel Dutta Chowdhury and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Typically it is seen that multimodal neural machine translation (MNMT) systems trained on a combination of visual and textual inputs produce better translations than systems trained using only textual inputs. The task of such systems can be decomposed into two sub-tasks: learning visually grounded representations from images and translation of the textual counterparts using those representations. In a multi-task learning framework, translations are generated from an attention-based encoder-decoder framework and grounded representations that are learned from pretrained convolutional neural networks (CNNs) for classifying images. In this thesis, I study different computational techniques to translate the meaning of sentences from one language into another considering the visual modality as a naturally occurring meaning representation bridging between languages. We examine the behaviour of state-of-the-art MNMT systems from the data perspective in order to understand the role of the both textual and visual inputs in such systems. We evaluate our models on the Multi30k, a large-scale multilingual multimodal dataset publicly available for machine learning research. Our results in the optimal and sparse data settings show that the differences in translation system performance are proportional to the amount of both visual and linguistic information whereas, in the adversarial condition the effect of the visual modality is rather small or negligible. The chapters of the thesis follow a progression starting with using different state-of-the-art MMT models for incorporating images in optimal data settings to creating synthetic image data under the low-resource scenario and extending to addition of adversarial perturbations to the textual input for evaluating the real contribution of images.
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 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:
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 Towards Responsible Machine Translation written by Helena Moniz and published by Springer Nature. This book was released on 2023-03-01 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a contribution to the research community towards thinking and reflecting on what Responsible Machine Translation really means. It was conceived as an open dialogue across disciplines, from philosophy to law, with the ultimate goal of providing a wide spectrum of topics to reflect on. It covers aspects related to the development of Machine translation systems, as well as its use in different scenarios, and the societal impact that it may have. This text appeals to students and researchers in linguistics, translation, natural language processing, philosophy, and law as well as professionals working in these fields.
Download or read book Hybrid Approaches to Machine Translation written by Marta R. Costa-jussà and published by Springer. This book was released on 2016-07-12 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures. The book is of interest primarily to MT specialists, but also – in the wider fields of Computational Linguistics, Machine Learning and Data Mining – to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.
Download or read book Dual Learning written by Tao Qin and published by Springer Nature. This book was released on 2020-11-13 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis,question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech synthesis and recognition, (visual) question answering and generation, image captioning and generation, and code summarization and generation. Offering a systematic and comprehensive overview of dual learning, this book enables interested researchers (both established and newcomers) and practitioners to gain a better understanding of the state of the art in the field. It also provides suggestions for further reading and tools to help readers advance the area. The book is divided into five parts. The first part gives a brief introduction to machine learning and deep learning. The second part introduces the algorithms based on the dual reconstruction principle using machine translation, image translation, speech processing and other NLP/CV tasks as the demo applications. It covers algorithms, such as dual semi-supervised learning, dual unsupervised learning and multi-agent dual learning. In the context of image translation, it introduces algorithms including CycleGAN, DualGAN, DiscoGAN cdGAN and more recent techniques/applications. The third part presents various work based on the probability principle, including dual supervised learning and dual inference based on the joint-probability principle and dual semi-supervised learning based on the marginal-probability principle. The fourth part reviews various theoretical studies on dual learning and discusses its connections to other learning paradigms. The fifth part provides a summary and suggests future research directions.
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 Learning Machine Translation written by Cyril Goutte and published by MIT Press. This book was released on 2009 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.
Download or read book Machine Learning in Translation Corpora Processing written by Krzysztof Wolk and published by CRC Press. This book was released on 2019-02-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.
Download or read book Machine Translation with Minimal Reliance on Parallel Resources written by George Tambouratzis and published by Springer. This book was released on 2017-08-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 Natural Language Processing and Chinese Computing written by Fei Liu and published by Springer Nature. This book was released on 2023-10-07 with total page 897 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set constitutes the refereed proceedings of the 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, held in Foshan, China, during October 12–15, 2023. The 143 regular papers included in these proceedings were carefully reviewed and selected from 478 submissions. They were organized in topical sections as follows: dialogue systems; fundamentals of NLP; information extraction and knowledge graph; machine learning for NLP; machine translation and multilinguality; multimodality and explainability; NLP applications and text mining; question answering; large language models; summarization and generation; student workshop; and evaluation workshop.
