Improving Neural Machine Translation Models with Monolingual Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Improving Neural Machine Translation Models with Monolingual Data PDF full book. Access full book title Improving Neural Machine Translation Models with Monolingual Data by Rico Sennrich. Download full books in PDF and EPUB format.
Author: Rico Sennrich
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Rico Sennrich
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 1108497322
Category : Computers
Languages : en
Pages : 409
Get Book Here
Book Description
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Author: Yong Cheng
Publisher: Springer Nature
ISBN: 9813297484
Category : Computers
Languages : en
Pages : 78
Get Book Here
Book Description
This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.
Author: Sergei Nirenburg
Publisher: IOS Press
ISBN: 9789051990744
Category : Computers
Languages : en
Pages : 338
Get Book Here
Book Description
Author: Prajit Dhar
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 1108601766
Category : Computers
Languages : en
Pages : 410
Get Book Here
Book Description
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
Author: Tao Qin
Publisher: Springer Nature
ISBN: 9811588848
Category : Computers
Languages : en
Pages : 190
Get Book Here
Book Description
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.
Author: Toan Q. Nguyen
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
Get Book Here
Book Description
Author: Jiajun Chen
Publisher: Springer
ISBN: 9811330832
Category : Computers
Languages : en
Pages : 125
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 14th China Workshop on Machine Translation, CWMT 2018, held in Wuyishan, China, in October 2018. The 9 papers presented in this volume were carefully reviewed and selected from 17 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
Author: Jinsong Su
Publisher: Springer Nature
ISBN: 9811675120
Category : Computers
Languages : en
Pages : 137
Get Book Here
Book Description
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.
