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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: 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: 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: 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: 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: Sharon Oviatt Publisher: Morgan & Claypool ISBN: 1970001739 Category : Computers Languages : en Pages : 815
Book Description
The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces-user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces. This three-volume handbook is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This third volume focuses on state-of-the-art multimodal language and dialogue processing, including semantic integration of modalities. The development of increasingly expressive embodied agents and robots has become an active test bed for coordinating multimodal dialogue input and output, including processing of language and nonverbal communication. In addition, major application areas are featured for commercializing multimodal-multisensor systems, including automotive, robotic, manufacturing, machine translation, banking, communications, and others. These systems rely heavily on software tools, data resources, and international standards to facilitate their development. For insights into the future, emerging multimodal-multisensor technology trends are highlighted in medicine, robotics, interaction with smart spaces, and similar areas. Finally, this volume discusses the societal impact of more widespread adoption of these systems, such as privacy risks and how to mitigate them. The handbook chapters provide a number of walk-through examples of system design and processing, information on practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this emerging field. In the final section of this volume, experts exchange views on a timely and controversial challenge topic, and how they believe multimodal-multisensor interfaces need to be equipped to most effectively advance human performance during the next decade.
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: Yong Cheng Publisher: Springer Nature ISBN: 9813297484 Category : Computers Languages : en Pages : 78
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: P. C. Yuen Publisher: World Scientific ISBN: 9789810245948 Category : Computers Languages : en Pages : 288
Book Description
With the advance of speech, image and video technology, human-computer interaction (HCI) will reach a new phase.In recent years, HCI has been extended to human-machine communication (HMC) and the perceptual user interface (PUI). The final goal in HMC is that the communication between humans and machines is similar to human-to-human communication. Moreover, the machine can support human-to-human communication (e.g. an interface for the disabled). For this reason, various aspects of human communication are to be considered in HMC. The HMC interface, called a multimodal interface, includes different types of input methods, such as natural language, gestures, face and handwriting characters.The nine papers in this book have been selected from the 92 high-quality papers constituting the proceedings of the 2nd International Conference on Multimodal Interface (ICMI '99), which was held in Hong Kong in 1999. The papers cover a wide spectrum of the multimodal interface.
Author: Fabio Alves Publisher: Routledge ISBN: 1351712454 Category : Language Arts & Disciplines Languages : en Pages : 734
Book Description
The Routledge Handbook of Translation and Cognition provides a comprehensive, state-of-the-art overview of how translation and cognition relate to each other, discussing the most important issues in the fledgling sub-discipline of Cognitive Translation Studies (CTS), from foundational to applied aspects. With a strong focus on interdisciplinarity, the handbook surveys concepts and methods in neighbouring disciplines that are concerned with cognition and how they relate to translational activity from a cognitive perspective. Looking at different types of cognitive processes, this volume also ventures into emergent areas such as neuroscience, artificial intelligence, cognitive ergonomics and human–computer interaction. With an editors’ introduction and 30 chapters authored by leading scholars in the field of Cognitive Translation Studies, this handbook is the essential reference and resource for students and researchers of translation and cognition and will also be of interest to those working in bilingualism, second-language acquisition and related areas.
Author: Koel Dutta Chowdhury
Author: Koel Dutta Chowdhury
Author: Alejandro Héctor Toselli
Author: Malek Mgaidi
Author: Zhiwen Tang
Author: Sharon Oviatt
Author: Ozan Caglayan
Author: Philipp Koehn
Author: Yong Cheng
Author: P. C. Yuen
Author: Fabio Alves