Efficient Neural Machine Translation
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Neural Machine Translation
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Author : Philipp Koehn
language : en
Publisher: Cambridge University Press
Release Date : 2020-06-18
Neural Machine Translation written by Philipp Koehn and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-18 with Computers categories.
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Machine Translation Of Morphologically Rich Languages Using Deep Neural Networks
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Author : Peyman Passban
language : en
Publisher:
Release Date : 2018
Machine Translation Of Morphologically Rich Languages Using Deep Neural Networks written by Peyman Passban and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This thesis addresses some of the challenges of translating morphologically rich languages (MRLs). Words in MRLs have more complex structures than those in other languages, so that a word can be viewed as a hierarchical structure with several internal subunits. Accordingly, word-based models in which words are treated as atomic units are not suitable for this set of languages. As a commonly used and eff ective solution, morphological decomposition is applied to segment words into atomic and meaning-preserving units, but this raises other types of problems some of which we study here. We mainly use neural networks (NNs) to perform machine translation (MT) in our research and study their diff erent properties. However, our research is not limited to neural models alone as we also consider some of the difficulties of conventional MT methods. First we try to model morphologically complex words (MCWs) and provide better word-level representations. Words are symbolic concepts which are represented numerically in order to be used in NNs. Our first goal is to tackle this problem and find the best representation for MCWs. In the next step we focus on language modeling (LM) and work at the sentence level. We propose new morpheme-segmentation models by which we finetune existing LMs for MRLs. In this part of our research we try to find the most efficient neural language model for MRLs. After providing word- and sentence-level neural information in the first two steps, we try to use such information to enhance the translation quality in the statistical machine translation (SMT) pipeline using several diff erent models. Accordingly, the main goal in this part is to find methods by which deep neural networks (DNNs) can improve SMT. One of the main interests of the thesis is to study neural machine translation (NMT) engines from diff erent perspectives, and finetune them to work with MRLs. In the last step we target this problem and perform end-to-end sequence modeling via NN-based models. NMT engines have recently improved significantly and perform as well as state-of-the-art systems, but still have serious problems with morphologically complex constituents. This shortcoming of NMT is studied in two separate chapters in the thesis, where in one chapter we investigate the impact of diff erent non-linguistic morpheme-segmentation models on the NMT pipeline, and in the other one we benefit from a linguistically motivated morphological analyzer and propose a novel neural architecture particularly for translating from MRLs. Our overall goal for this part of the research is to find the most suitable neural architecture to translate MRLs. We evaluated our models on diff erent MRLs such as Czech, Farsi, German, Russian, and Turkish, and observed significant improvements. The main goal targeted in this research was to incorporate morphological information into MT and define architectures which are able to model the complex nature of MRLs. The results obtained from our experimental studies confirm that we were able to achieve our goal.
Machine Translation
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Author : Jiajun Chen
language : en
Publisher: Springer
Release Date : 2019-01-08
Machine Translation written by Jiajun Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-08 with Computers categories.
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.
Joint Training For Neural Machine Translation
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Author : Yong Cheng
language : en
Publisher: Springer Nature
Release Date : 2019-08-26
Joint Training For Neural Machine Translation written by Yong Cheng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-26 with Computers categories.
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.
Neural Machine Translation From Kashmiri To English
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Author : Nawaz Ali Lone
language : en
Publisher: Mohd Abdul Hafi
Release Date : 2024-03-30
Neural Machine Translation From Kashmiri To English written by Nawaz Ali Lone and has been published by Mohd Abdul Hafi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-30 with Computers categories.
Natural Language Processing (NLP) is the branch of computer science which involves the use of computers or automatic machines to process natural or human languages in textual as well as audio form in a similar manner as humans do. It is the application of computational techniques to learn, understand, and produce human language content [1]. The main aim of Natural Language Processing is the design & development of software tools with capability of generation, understanding, and analyzing natural or human languages. Natural Language Processing has emerged as a combination of artificial intelligence and linguistics since 1950s [2]. NLP which mainly expounds on how a machine or computer may be used to interpret and manipulate natural languages is considered to be a very significant and essential research area in Computer Science and Computational Linguistics. NLP has its roots in many disciplines, including information science, computer science, mathematics, linguistics, and so on [3]. It combines various frameworks such as rule-based, statistical, and machine learning approaches to strive for efficiency across different parameters of interest. Integrating various such approaches provide a better understanding of the content involved and help in processing the content with good accuracy. Natural Language Processing is focused to make computer programs, procedures, applications that translate text across different languages, be responsive to spoken content, text summarization, etc. The languages which are the hallmark of cultural diversity are ambiguous in nature [6], therefore it becomes challenging to create procedures that will accurately decipher the actual meaning of text or voice content.
Machine Translation And Transliteration Involving Related Low Resource Languages
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Author : Anoop Kunchukuttan
language : en
Publisher: CRC Press
Release Date : 2021-09-08
Machine Translation And Transliteration Involving Related Low Resource Languages written by Anoop Kunchukuttan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-08 with Computers categories.
Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.
Machine Translation
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Author : Derek F. Wong
language : en
Publisher: Springer
Release Date : 2017-11-13
Machine Translation written by Derek F. Wong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-13 with Computers categories.
This book constitutes the refereed proceedings of the 13th China Workshop on Machine Translation, CWMT 2017, held in Dalian, China, in September 2017. The 10 papers presented in this volume were carefully reviewed and selected from 26 submissions and focus on all aspects of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.
Dual Learning
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Author : Tao Qin
language : en
Publisher: Springer Nature
Release Date : 2020-11-13
Dual Learning written by Tao Qin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
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.
Machine Translation
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Author : Shujian Huang
language : en
Publisher: Springer Nature
Release Date : 2019-11-22
Machine Translation written by Shujian Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-22 with Computers categories.
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.
Neural Machine Translation For Multimodal Interaction
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Author : Koel Dutta Chowdhury
language : en
Publisher:
Release Date : 2019
Neural Machine Translation For Multimodal Interaction written by Koel Dutta Chowdhury and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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.