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Author: Zhiwen Tang Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this project, I proposed a set of methods to complete the task of multimodal machine translation, which is to generate a image caption in the target language given the image itself and corresponding image captions in the source language. I completed this task with deep learning techniques.
Author: Zhiwen Tang Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this project, I proposed a set of methods to complete the task of multimodal machine translation, which is to generate a image caption in the target language given the image itself and corresponding image captions in the source language. I completed this task with deep learning techniques.
Author: Koel Dutta Chowdhury Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Malek Mgaidi Publisher: ISBN: Category : Languages : en Pages :
Book Description
Neural Machine Translation is a newly emerging approach to machine translation which attempts to build and train a large neural network that reads a sentence and outputs a notable translation. Nowadays the performance of such machines is in- creasingly in demand and Multilingual Neutral Machine Translation has emerged. There is an abundant bibliography on Multimodal Neutral Machine Translation as there is a consistent number of models which differ by the final aspects of trans- lation (adequacy, fidelity and fluency) and the multitude of inputs they can use (images, videos, text, speech or a combination of them). The GroundedTranslation was chosen in this work. As we know, by far, the state- of-the art provides some techniques such as using Long Short Term Memory and an encoder-decoder architecture for example to solve some training problems and they already have been implemented by Elliot Desmond for this retained solution. However, no investigation has been oriented toward the optimizer. This work aims to study multimodal neural machine translation architectures and its behavior un- der different optimization algorithms.
Author: Ozan Caglayan Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Machine translation aims at automatically translating documents from one language to another without human intervention. With the advent of deep neural networks (DNN), neural approaches to machine translation started to dominate the field, reaching state-ofthe-art performance in many languages. Neural machine translation (NMT) also revived the interest in interlingual machine translation due to how it naturally fits the task into an encoder-decoder framework which produces a translation by decoding a latent source representation. Combined with the architectural flexibility of DNNs, this framework paved the way for further research in multimodality with the objective of augmenting the latent representations with other modalities such as vision or speech, for example. This thesis focuses on a multimodal machine translation (MMT) framework that integrates a secondary visual modality to achieve better and visually grounded language understanding. I specifically worked with a dataset containing images and their translated descriptions, where visual context can be useful forword sense disambiguation, missing word imputation, or gender marking when translating from a language with gender-neutral nouns to one with grammatical gender system as is the case with English to French. I propose two main approaches to integrate the visual modality: (i) a multimodal attention mechanism that learns to take into account both sentence and convolutional visual representations, (ii) a method that uses global visual feature vectors to prime the sentence encoders and the decoders. Through automatic and human evaluation conducted on multiple language pairs, the proposed approaches were demonstrated to be beneficial. Finally, I further show that by systematically removing certain linguistic information from the input sentences, the true strength of both methods emerges as they successfully impute missing nouns, colors and can even translate when parts of the source sentences are completely removed.
Author: Alejandro Héctor Toselli Publisher: Springer Science & Business Media ISBN: 0857294792 Category : Computers Languages : en Pages : 281
Book Description
This book presents a different approach to pattern recognition (PR) systems, in which users of a system are involved during the recognition process. This can help to avoid later errors and reduce the costs associated with post-processing. The book also examines a range of advanced multimodal interactions between the machine and the users, including handwriting, speech and gestures. Features: presents an introduction to the fundamental concepts and general PR approaches for multimodal interaction modeling and search (or inference); provides numerous examples and a helpful Glossary; discusses approaches for computer-assisted transcription of handwritten and spoken documents; examines systems for computer-assisted language translation, interactive text generation and parsing, relevance-based image retrieval, and interactive document layout analysis; reviews several full working prototypes of multimodal interactive PR applications, including live demonstrations that can be publicly accessed on the Internet.
Author: Fouad Sabry Publisher: One Billion Knowledgeable ISBN: Category : Computers Languages : en Pages : 133
Book Description
What Is Machine Translation The subfield of computational linguistics known as machine translation, which is often referred to by the abbreviation MT at times, explores the use of software to translate text or speech from one language to another. Machine translation can also be referred to as automatic translation. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Machine Translation Chapter 2: Computational Linguistics Chapter 3: Natural Language Processing Chapter 4: Statistical Machine Translation Chapter 5: Neural Machine Translation Chapter 6: Google Neural Machine Translation Chapter 7: Hybrid Machine Translation Chapter 8: Rule-based Machine Translation Chapter 9: Evaluation of Machine Translation Chapter 10: History of Machine Translation (II) Answering the public top questions about machine translation. (III) Real world examples for the usage of machine translation in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of machine translation' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of machine translation.
Author: Philipp Koehn Publisher: Cambridge University Press ISBN: 1108601766 Category : Computers Languages : en Pages : 410
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: Derek F. Wong Publisher: Springer ISBN: 9811071349 Category : Computers Languages : en Pages : 135
Book Description
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.
Author: Zhiwen Tang
Author: Zhiwen Tang
Author: Koel Dutta Chowdhury
Author: Malek Mgaidi
Author: Philipp Koehn
Author: Ozan Caglayan
Author: Alejandro Héctor Toselli
Author: Fouad Sabry
Author: Philipp Koehn
Author: Aboul Ella Hassanien
Author: Derek F. Wong