PURPOSE : To provide translation patterns for machine translation which a user can easily generate and efficiently retrieve . CONSTITUTION : A translation pattern 18 consists of an original language pattern 18A , a variable 18B obtained by substituting this original language pattern with one character , and an objective language pattern 18C as a translation corresponding to the original language pattern . Machine translation has been the dominant paradigm for automated multilingual document production . In this paradigm , a technical writer generates a source text , which is translated by the computer system into another language and then edited . The requirements made of the target language were that it be parenthesisfree , that it contain a minimum number of instruction types , and that it would simplify the translation process . The feasibility of this language as target language to simplify the machine translation process and the direct implementation of this language on a computer by means of suitable hardware is considered . ( C.H. ) Finally , based on the combination algorithm , the fragment-combination-bank including all the possible fragment-combination forms of every parse tree in the treebank is built . Based on the successful construction of the knowledge source , the whole machine translation process can be implemented efficiently and accurately . Machine language translation as it stands today relies primarily on rule-based methods , which use a direct dictionary translation and at best attempts to rearrange the words in a sentence to follow the translation language ’s grammar rules to allow for better parsing on the part of the user . This project seeks to implement a rule-based translation from German to English , for users who are only fluent in one of the languages . Developing an automatic system to identify speakers as native or non-native , as well as their native language , including dialect , within input audio streams , is the major goal of this project . Such a system may be used alone or with other downstream applications such as machine language translation systems . In this paper we present four approaches to identify native and non-native speakers as a binary recognition problem . Purpose of this paper is to present the experience and findings of international collaborative research conducted over several years on vision-based sign language translation . At first , this paper extends a discussion on sign language and its variations , machine-based translation and its significance . Secondly , a discussion on how three main tasks within the machine translation , namely : a ) hand localization and tracking , b ) hand posture interpretation , and c ) hand gesture interpretation , can be addressed . The objective of this research is to find out the potentiality of machine based translation , such as Google Translation for example , in translating fixed expressions . The framework for fixed expression will be based on Baker ’s typology of equivalence ( 1992 ) and Alexander ’s classification ( 1978 ) . At the intersection of rule - based and statistical methods , hybrid systems aim at leveraging human contributed rules and corpus statistics [ reference ][ reference ][ reference ] . Our approach is inspired by the recent success of neural language models for image captioning [ reference ][ reference ][ reference ][ reference ] , machine translation [ reference ][ reference ][ reference ] , and modeling conversations and dialogues [ reference ][ reference ][ reference ] . Our model is most similar to [ reference ] who use an encoder - decoder style neural network model to tackle the WEATHERGOV and ROBOCUP tasks . In this paper , we solve the VQA problem using a multimodal memory network architecture that applies a spatial attention mechanism over an input image guided by an input text question . The neural attention mechanism has been widely used in different areas of computer vision and natural language processing , see for example the attention models in image captioning , video description generation , machine translation and machine reading systems . Most methods use the soft attention mechanism first proposed in , which adds a layer to the network that predicts soft weights and uses them to compute a weighted combination of the items in memory . The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence . The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder – Decoder as an additional feature in the existing log - linear model . Qualitatively , we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases . These include , but are not limited to , language modeling , paraphrase detection and word embedding extraction . In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results . summarizes a successful usage of feedforward neural networks in the framework of phrase - based SMT system . It is also possible to learn a linear matrix to translate words and phrases between languages . These properties make word vectors attractive for many natural language processing tasks such as language modeling , natural language understanding , statistical machine translation , image understanding and relational extraction . subsection : Paragraph Vector : A distributed memory model Tissue-engineered bone grafts created by in vitro manipulation of bioscaffolds , seed cells , and growth factors have been considered potential treatments for bone defect reconstruction . However , a significant gap remains between experimental successes and clinical translation . An emerging strategy for bridging this gap is using the in vivo bioreactor principle and flap prefabrication techniques . They discuss the evolution of clinical NLP from early innovation to stable research at major clinical centers to a shift toward mainstream interest in biomedical NLP . Motivated by the demand for clinical language processing , the BioNLP community has shown renewed interest in the development of fundamental NLP methods and advances in NLP systems for clinical decision support . Another contributor to the growth of BioNLP has been the availability of a wide range of resources suitable for analysis or tools for assisting in such analyses . Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation . Advances in clinical language processing have made i ... In this chapter , after briefly discussing about the technology and its market , we will discuss about important components in a photoacoustic imaging system ( light source , ultrasound probes , DAQ etc . ) and conclude about the key strategies in this direction and the improvements that may potentially help for fast clinical translation . In the next part , we elaborate about the major steps in the clinical translation process — focusing on key opportunities and challenges to be solved . MedExtractR was developed on notes from Vanderbilt University Medical Center , using medications prescribed with varying complexity . We evaluated medExtractR and compared it with 3 existing systems : MedEx , MedXN , and CLAMP ( Clinical Language Annotation , Modeling , and Processing ) . We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III ( Medical Information Mart for Intensive Care III ) database . However , end users often have to customize existing systems for their individual tasks , which can require substantial NLP skills . Here we present CLAMP ( Clinical Language Annotation , Modeling , and Processing ) , a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components , but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications . Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding . In this paper , we explore automated approaches for scoring semantic fluency responses that leverage ontological resources and distributional semantic models to characterize the semantic fluency responses produced by young children with and without ASD . Using these methods , we find significant differences in the semantic fluency responses of children with ASD , demonstrating the utility of using objective methods for clinical language analysis . This presentation explores the latest in this technological innovation in speech recognition and natural language understanding and its application in the complex busy clinical setting . Combining standard speech recognition with clinical language understanding opens up a whole new opportunity to simplify the human computer interface and relieve clinicians from challenge of learning and using complex electronic medical records allowing them to focus their time where it is best used , on the patient . A recent survey of clinicians revealed that : " 80 percent of physicians believe that within five years , virtual assistants will drastically change how they interact and use electronic health records and other healthcare apps , making them more efficient and freeing up time to spend on patients " We have already seen the impact of this technology in our interactions with mobile devices . It can therefore deliver optical visualization of cancer far deeper in tissue than optical microscopy and other conventional optical imaging methods . Technological progress and novel contrast media have resulted in optoacoustic imaging being propagated to basic cancer research and in clinical translation projects . We briefly review recent technological advances , showcase the ability to resolve unique cancer biomarkers based on spectral features at different imaging scales , and highlight the imaging performance achieved in preclinical and clinical imaging applications . . The papers published in JTEHM are accessible through IEEE Xplore and will be in PubMed soon , as well as in downloadable PDF versions with additional material . We welcome submission of your research findings and clinical translation studies . However , these methods are limited by expert curation , which is hard to scale and has trouble generalizing to unseen datasets that do not share an overlapping vocabulary . In contrast , we approach the clinical word and sentence translation problem in a completely unsupervised manner . We show that a framework using representation learning , bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation , and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability , respectively , on sentence translation . In this perspective , we discuss the current advances made towards bioprinting of composite osteochondral tissues and present an account of challenges — in terms of tissue integration , long-term survival , and mechanical strength at the time of implantation — required to be addressed for effective clinical translation of bioprinted tissues . Finally , we highlight some of the future trends related to osteochondral bioprinting with the hope of in-clinical translation . Translation is an activity that is growing ever more complex and can not be accounted for in linguistic terms any longer . Given the lack of a general picture of multimodal translation in the literature , a new study is needed to move towards a more comprehensive understanding of multimodal translation . This book offers a model for multimodal ST analysis that can be used as a tool to improve our understanding of how multimodal texts are organised to convey meaning , and of what this means for their translation . The promise of combining vision and language in multimodal machine translation is that systems will produce better translations by leveraging the image data . However , inconsistent results have lead to uncertainty about whether the images actually improve translation quality . It is a unique task given that in the field of machine translation , many state-of-the-arts algorithms still only employ textual information . In this work , we explore the effectiveness of reinforcement learning in multimodal machine translation . We present a novel algorithm based on the Advantage Actor-Critic ( A2C ) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation ( WMT18 ) . The diversity of the multimodal landscape brings about research challenges that must be carefully addressed to ensure that these research efforts yield useful and credible results . This special issue is dedicated to a discussion on how to engage in multimodal translation research : how traditional research methods can be adapted and what kinds of novel approaches can be adopted or developed in order to deal with a diversity of multimodal data . In this introduction , we first discuss definitions of mode and multimodality and reflect on the nature of multimodality as a topic of research within Translation Studies . We then explain our rationale for dedicating the special issue to research methods and introduce three areas of multimodal translation research that , in our view , merit particular attention from a methodological point of view . Finally , we introduce the articles contained in this special issue . We also propose a method to customize the personal face model by using our GUI tool . By combining these techniques and the translated voice synthesis technique , an automatic multimodal translation can be achieved that is suitable for video mail or automatic dubbing systems into other languages . In this paper , we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT . Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision . The experimental results on the widely used Multi30 K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time . To formalize this fact , a new framework , which integrates the human activity into the recognition process taking advantage of the user 's feedback , is described . Several applications , involving Interactive Speech Transcription and Multimodal Interactive Machine Translation , have recently been considered under this framework . These applications are reviewed in this paper , and some experiments , showing that the proposed framework can save significant amounts of human effort , are also presented . 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 . In this talk , I would like to introduce brief history of the speech-to-speech translation research and state-of-the-art speech translation system . Then I would introduce new challenges toward a simultaneous speech-to-speech translation including simultaneous incremental machine translation , joint-optimization of ASR and MT modules , Speech-to-speech translation preserving para/non-linguistic information and multimodal speech translation with a talking face . Typically the answer is no. However , new deep architectures can generate RGB images from data acquired in a different modality , such as depth data . In this paper , we propose a new Deterministic Conditional GAN , trained on annotated RGB-D face datasets , effective for a face-to-face translation from depth to RGB . Although the network can not reconstruct the exact somatic features for unknown individual faces , it is capable to reconstruct plausible faces ; their appearance is accurate enough to be used in many pattern recognition tasks . To address the problem , we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs . We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks , including street scene image translation , animal image translation , and face image translation . We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets . To mitigate this limitation , in this paper , we proposed a Siamese-Network based residual attributes learning model to learn the attributes difference in the high-level latent space . Compared to existing models that perform attributes editing based on an attributes classifier , the proposed deep residual attributes learning model utilized relatively weaker information of attribute differences for face image translation . Sufficient qualitative and quantitative experiments conducted on CelebA dataset proved the effectiveness of our proposed method , moreover , we also adopt the proposed residual attributes learning model in two state-of-the-art models under different data usage percentage to show the effectiveness of the proposed model on boosting attribute editing performance under limited data usage . Throughout the paper , two important contributions are presented . First , since a classifier based approach is used , the proposed method automatically selects the most similar artist and makes the face translation . Second , so as to obtain the latent information about faces , a pre-trained model is utilized at the encoder part . Internationalization is a common practice today in software development . In the most basic sense , internationalization is carried out by applying localization design guidelines to face language translation , icon representation , character sets and so on . However , this practice is mostly intended for design purposes , which results insufficient when applying internationalization in huge projects and , specifically , through a concrete development process . In such a narrow domain , most utterances tend to be short , having 1 to 3 keywords , and since it is intended for use in face-to-face situations , the users can share common situational information with each other . As such , this paper focuses on a face-to-face speech translation system in the traveler support domain . Abstract
PURPOSE : To provide translation patterns for machine translation which a user can easily generate and efficiently retrieve . CONSTITUTION : A translation pattern 18 consists of an original language pattern 18A , a variable 18B obtained by substituting this original language pattern with one character , and an objective language pattern 18C as a translation corresponding to the original language pattern .
Machine translation has been the dominant paradigm for automated multilingual document production . In this paradigm , a technical writer generates a source text , which is translated by the computer system into another language and then edited .
The requirements made of the target language were that it be parenthesisfree , that it contain a minimum number of instruction types , and that it would simplify the translation process . The feasibility of this language as target language to simplify the machine translation process and the direct implementation of this language on a computer by means of suitable hardware is considered . ( C.H. )
Finally , based on the combination algorithm , the fragment-combination-bank including all the possible fragment-combination forms of every parse tree in the treebank is built . Based on the successful construction of the knowledge source , the whole machine translation process can be implemented efficiently and accurately .
Machine language translation as it stands today relies primarily on rule-based methods , which use a direct dictionary translation and at best attempts to rearrange the words in a sentence to follow the translation language ’s grammar rules to allow for better parsing on the part of the user . This project seeks to implement a rule-based translation from German to English , for users who are only fluent in one of the languages .
Developing an automatic system to identify speakers as native or non-native , as well as their native language , including dialect , within input audio streams , is the major goal of this project . Such a system may be used alone or with other downstream applications such as machine language translation systems . In this paper we present four approaches to identify native and non-native speakers as a binary recognition problem .
Purpose of this paper is to present the experience and findings of international collaborative research conducted over several years on vision-based sign language translation . At first , this paper extends a discussion on sign language and its variations , machine-based translation and its significance . Secondly , a discussion on how three main tasks within the machine translation , namely : a ) hand localization and tracking , b ) hand posture interpretation , and c ) hand gesture interpretation , can be addressed .
The objective of this research is to find out the potentiality of machine based translation , such as Google Translation for example , in translating fixed expressions . The framework for fixed expression will be based on Baker ’s typology of equivalence ( 1992 ) and Alexander ’s classification ( 1978 ) .
At the intersection of rule - based and statistical methods , hybrid systems aim at leveraging human contributed rules and corpus statistics [ reference ][ reference ][ reference ] . Our approach is inspired by the recent success of neural language models for image captioning [ reference ][ reference ][ reference ][ reference ] , machine translation [ reference ][ reference ][ reference ] , and modeling conversations and dialogues [ reference ][ reference ][ reference ] . Our model is most similar to [ reference ] who use an encoder - decoder style neural network model to tackle the WEATHERGOV and ROBOCUP tasks .
In this paper , we solve the VQA problem using a multimodal memory network architecture that applies a spatial attention mechanism over an input image guided by an input text question . The neural attention mechanism has been widely used in different areas of computer vision and natural language processing , see for example the attention models in image captioning , video description generation , machine translation and machine reading systems . Most methods use the soft attention mechanism first proposed in , which adds a layer to the network that predicts soft weights and uses them to compute a weighted combination of the items in memory .
The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence . The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder – Decoder as an additional feature in the existing log - linear model . Qualitatively , we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases .
These include , but are not limited to , language modeling , paraphrase detection and word embedding extraction . In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results . summarizes a successful usage of feedforward neural networks in the framework of phrase - based SMT system .
It is also possible to learn a linear matrix to translate words and phrases between languages . These properties make word vectors attractive for many natural language processing tasks such as language modeling , natural language understanding , statistical machine translation , image understanding and relational extraction . subsection : Paragraph Vector : A distributed memory model
Tissue-engineered bone grafts created by in vitro manipulation of bioscaffolds , seed cells , and growth factors have been considered potential treatments for bone defect reconstruction . However , a significant gap remains between experimental successes and clinical translation . An emerging strategy for bridging this gap is using the in vivo bioreactor principle and flap prefabrication techniques .
They discuss the evolution of clinical NLP from early innovation to stable research at major clinical centers to a shift toward mainstream interest in biomedical NLP . Motivated by the demand for clinical language processing , the BioNLP community has shown renewed interest in the development of fundamental NLP methods and advances in NLP systems for clinical decision support . Another contributor to the growth of BioNLP has been the availability of a wide range of resources suitable for analysis or tools for assisting in such analyses .
Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation . Advances in clinical language processing have made i ...
In this chapter , after briefly discussing about the technology and its market , we will discuss about important components in a photoacoustic imaging system ( light source , ultrasound probes , DAQ etc . ) and conclude about the key strategies in this direction and the improvements that may potentially help for fast clinical translation . In the next part , we elaborate about the major steps in the clinical translation process — focusing on key opportunities and challenges to be solved .
MedExtractR was developed on notes from Vanderbilt University Medical Center , using medications prescribed with varying complexity . We evaluated medExtractR and compared it with 3 existing systems : MedEx , MedXN , and CLAMP ( Clinical Language Annotation , Modeling , and Processing ) . We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III ( Medical Information Mart for Intensive Care III ) database .
However , end users often have to customize existing systems for their individual tasks , which can require substantial NLP skills . Here we present CLAMP ( Clinical Language Annotation , Modeling , and Processing ) , a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components , but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications . Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding .
In this paper , we explore automated approaches for scoring semantic fluency responses that leverage ontological resources and distributional semantic models to characterize the semantic fluency responses produced by young children with and without ASD . Using these methods , we find significant differences in the semantic fluency responses of children with ASD , demonstrating the utility of using objective methods for clinical language analysis .
This presentation explores the latest in this technological innovation in speech recognition and natural language understanding and its application in the complex busy clinical setting . Combining standard speech recognition with clinical language understanding opens up a whole new opportunity to simplify the human computer interface and relieve clinicians from challenge of learning and using complex electronic medical records allowing them to focus their time where it is best used , on the patient . A recent survey of clinicians revealed that : " 80 percent of physicians believe that within five years , virtual assistants will drastically change how they interact and use electronic health records and other healthcare apps , making them more efficient and freeing up time to spend on patients " We have already seen the impact of this technology in our interactions with mobile devices .
It can therefore deliver optical visualization of cancer far deeper in tissue than optical microscopy and other conventional optical imaging methods . Technological progress and novel contrast media have resulted in optoacoustic imaging being propagated to basic cancer research and in clinical translation projects . We briefly review recent technological advances , showcase the ability to resolve unique cancer biomarkers based on spectral features at different imaging scales , and highlight the imaging performance achieved in preclinical and clinical imaging applications . .
The papers published in JTEHM are accessible through IEEE Xplore and will be in PubMed soon , as well as in downloadable PDF versions with additional material . We welcome submission of your research findings and clinical translation studies .
However , these methods are limited by expert curation , which is hard to scale and has trouble generalizing to unseen datasets that do not share an overlapping vocabulary . In contrast , we approach the clinical word and sentence translation problem in a completely unsupervised manner . We show that a framework using representation learning , bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation , and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability , respectively , on sentence translation .
In this perspective , we discuss the current advances made towards bioprinting of composite osteochondral tissues and present an account of challenges — in terms of tissue integration , long-term survival , and mechanical strength at the time of implantation — required to be addressed for effective clinical translation of bioprinted tissues . Finally , we highlight some of the future trends related to osteochondral bioprinting with the hope of in-clinical translation .
Translation is an activity that is growing ever more complex and can not be accounted for in linguistic terms any longer . Given the lack of a general picture of multimodal translation in the literature , a new study is needed to move towards a more comprehensive understanding of multimodal translation . This book offers a model for multimodal ST analysis that can be used as a tool to improve our understanding of how multimodal texts are organised to convey meaning , and of what this means for their translation .
The promise of combining vision and language in multimodal machine translation is that systems will produce better translations by leveraging the image data . However , inconsistent results have lead to uncertainty about whether the images actually improve translation quality .
It is a unique task given that in the field of machine translation , many state-of-the-arts algorithms still only employ textual information . In this work , we explore the effectiveness of reinforcement learning in multimodal machine translation . We present a novel algorithm based on the Advantage Actor-Critic ( A2C ) algorithm that specifically cater to the multimodal machine translation task of the EMNLP 2018 Third Conference on Machine Translation ( WMT18 ) .
The diversity of the multimodal landscape brings about research challenges that must be carefully addressed to ensure that these research efforts yield useful and credible results . This special issue is dedicated to a discussion on how to engage in multimodal translation research : how traditional research methods can be adapted and what kinds of novel approaches can be adopted or developed in order to deal with a diversity of multimodal data . In this introduction , we first discuss definitions of mode and multimodality and reflect on the nature of multimodality as a topic of research within Translation Studies . We then explain our rationale for dedicating the special issue to research methods and introduce three areas of multimodal translation research that , in our view , merit particular attention from a methodological point of view . Finally , we introduce the articles contained in this special issue .
We also propose a method to customize the personal face model by using our GUI tool . By combining these techniques and the translated voice synthesis technique , an automatic multimodal translation can be achieved that is suitable for video mail or automatic dubbing systems into other languages .
In this paper , we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT . Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision . The experimental results on the widely used Multi30 K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time .
To formalize this fact , a new framework , which integrates the human activity into the recognition process taking advantage of the user 's feedback , is described . Several applications , involving Interactive Speech Transcription and Multimodal Interactive Machine Translation , have recently been considered under this framework . These applications are reviewed in this paper , and some experiments , showing that the proposed framework can save significant amounts of human effort , are also presented .
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 .
In this talk , I would like to introduce brief history of the speech-to-speech translation research and state-of-the-art speech translation system . Then I would introduce new challenges toward a simultaneous speech-to-speech translation including simultaneous incremental machine translation , joint-optimization of ASR and MT modules , Speech-to-speech translation preserving para/non-linguistic information and multimodal speech translation with a talking face .
Typically the answer is no. However , new deep architectures can generate RGB images from data acquired in a different modality , such as depth data . In this paper , we propose a new Deterministic Conditional GAN , trained on annotated RGB-D face datasets , effective for a face-to-face translation from depth to RGB . Although the network can not reconstruct the exact somatic features for unknown individual faces , it is capable to reconstruct plausible faces ; their appearance is accurate enough to be used in many pattern recognition tasks .
To address the problem , we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs . We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks , including street scene image translation , animal image translation , and face image translation . We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets .
To mitigate this limitation , in this paper , we proposed a Siamese-Network based residual attributes learning model to learn the attributes difference in the high-level latent space . Compared to existing models that perform attributes editing based on an attributes classifier , the proposed deep residual attributes learning model utilized relatively weaker information of attribute differences for face image translation . Sufficient qualitative and quantitative experiments conducted on CelebA dataset proved the effectiveness of our proposed method , moreover , we also adopt the proposed residual attributes learning model in two state-of-the-art models under different data usage percentage to show the effectiveness of the proposed model on boosting attribute editing performance under limited data usage .
Throughout the paper , two important contributions are presented . First , since a classifier based approach is used , the proposed method automatically selects the most similar artist and makes the face translation . Second , so as to obtain the latent information about faces , a pre-trained model is utilized at the encoder part .
Internationalization is a common practice today in software development . In the most basic sense , internationalization is carried out by applying localization design guidelines to face language translation , icon representation , character sets and so on . However , this practice is mostly intended for design purposes , which results insufficient when applying internationalization in huge projects and , specifically , through a concrete development process .
In such a narrow domain , most utterances tend to be short , having 1 to 3 keywords , and since it is intended for use in face-to-face situations , the users can share common situational information with each other . As such , this paper focuses on a face-to-face speech translation system in the traveler support domain . Abstract
Translater based on pattern An Interactive Support Tool for Writing Multilingual Manuals A TRANSLATOR-ORIENTED INTERMEDIATE COMPUTER LANGUAGE Knowledge source construction in data-oriented English-Chinese machine translation TJHSST Computer Systems Lab Senior Research Project Development of a German-English Translator 2007-2008 An automatic non-native speaker recognition system Developing Automatic Markerless Sign Language Gesture Tracking and Recognition System Machine Translation and Fixed Expressions Neural Text Generation from Structured Data with Application to the Biography Domain Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Distributed Representations of Sentences and Documents Bone Graft Prefabrication Following the In Vivo Bioreactor Principle Current issues in biomedical text mining and natural language processing Extracting Clinical Findings from Swedish Health Record Text Clinical Translation of Photoacoustic Imaging—Opportunities and Challenges from an Industry Perspective medExtractR: A targeted, customizable approach to medication extraction from electronic health records CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines Vector space models for evaluating semantic fluency in autism Bringing an Intelligent Virtual Assistant to Healthcare Mesoscopic and macroscopic optoacoustic imaging of cancer. Introducing the IEEE Journal of Translational Engineering in Health and Medicine Unsupervised Clinical Language Translation Bioprinting of osteochondral tissues: A perspective on current gaps and future trends A New Model for Source Text Analysis in Translation Adversarial Evaluation of Multimodal Machine Translation Multimodal Machine Translation with Reinforcement Learning Why methods matter: Approaching multimodality in translation research Multimodal Translation System Using Texture-Mapped Lip-Sync Images for Video Mail and Automatic Dubbing Applications Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting Interactive Pattern Recognition Multimodal neural machine translation Keynote speech 3: Toward simultaneous, natural and multimodal speech-to-speech translation Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face Unsupervised Image-to-Image Translation Networks ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation YesilcamGAN: Automatic face translation to Yesilcam artists Integrating Internationalization in the User-Centered Software Development Process Usability considerations of speech-to-speech translation system
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HAVE EMNLP ECCV EMNLP ICML ACL (2) EMNLP MLMI O-COCOSDA/CASLRE ICPR SIU HCI (20) INTERSPEECH
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