While academic research is more and more focusing on integration of deep learning approaches for machine translation, also called Neural Machine Translation, and shows promising and exciting results – the resulting systems still have important pragmatic limitations compared to the current generation of translation engine. We will be discussing how SYSTRAN is integrating these new techniques into production systems, the results and benefits for the end users, and our vision for the next versions.
Mark Seligman of Spoken Translation, Inc. will provide some history of the speech translation field since the 1990s; give a quick tour of the state of the art; and speculate about future developments, with special interest in deep semantics.
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
Towards Universal Language Understanding (2020 version)Yunyao Li
Keynote talk given at Pacific Asia Conference on Language, Information and Computation (PACLIC 34) on Pacific Asia Conference on Language, Information and Computation (PACLIC 34) on October 24, 2020.
Title: Towards Universal Natural Language Understanding
Abstract:
Understanding the semantics of the natural language is a fundamental task in artificial intelligence. English semantic understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Compare and Comply. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in addressing these challenges in the past few years to provide the same unified semantic representation across languages. We will also showcase how such universal semantic understanding of natural languages can enable cross-lingual information extraction in concrete domains (e.g. insurance and compliance) and show promise towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
Adam Coates at AI Frontiers: AI for 100 Million People with Deep LearningAI Frontiers
Large scale deep learning has made it possible for small teams of researchers and engineers to tackle hard AI problems that previously entailed massive engineering efforts. Adam shares the story of Baidu’s Deep Speech engine: how a recurrent neural network has evolved into a state-of-the-art production speech recognition system in multiple languages, often exceeding the abilities of native speakers. He covers the vision, the implementation, and some lessons learned to illustrate what it takes to build new AI technology that 100 million people will care about.
Slides for talk given at Women in Engineering on March 20, 2021.
Abstract:
Natural language understanding is a fundamental task in artificial intelligence. English understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Discovery. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in the past few years in addressing these challenges. We will also showcase how universal semantic representation of natural languages can enable cross-lingual information extraction in concrete domains (e.g. compliance) and show ongoing efforts towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Mark Seligman of Spoken Translation, Inc. will provide some history of the speech translation field since the 1990s; give a quick tour of the state of the art; and speculate about future developments, with special interest in deep semantics.
Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)AI Frontiers
Spoken dialogue systems have nearly 30 years of history, which can be divided into three generations: symbolic-rule or template based (before late 90’s), statistical learning based, and deep learning based (since 2014). This talk will briefly survey the history of conversational systems, and analyze why and how the underlying technology moved from one generation to the next. Strengths and weaknesses of these three largely distinct types of bot technology are examined and future directions are discussed. Part of this talk is based on my recent article: How deep reinforcement learning can help chatbots, Venturebeat, Aug 2016.
Towards Universal Language Understanding (2020 version)Yunyao Li
Keynote talk given at Pacific Asia Conference on Language, Information and Computation (PACLIC 34) on Pacific Asia Conference on Language, Information and Computation (PACLIC 34) on October 24, 2020.
Title: Towards Universal Natural Language Understanding
Abstract:
Understanding the semantics of the natural language is a fundamental task in artificial intelligence. English semantic understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Compare and Comply. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in addressing these challenges in the past few years to provide the same unified semantic representation across languages. We will also showcase how such universal semantic understanding of natural languages can enable cross-lingual information extraction in concrete domains (e.g. insurance and compliance) and show promise towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
Adam Coates at AI Frontiers: AI for 100 Million People with Deep LearningAI Frontiers
Large scale deep learning has made it possible for small teams of researchers and engineers to tackle hard AI problems that previously entailed massive engineering efforts. Adam shares the story of Baidu’s Deep Speech engine: how a recurrent neural network has evolved into a state-of-the-art production speech recognition system in multiple languages, often exceeding the abilities of native speakers. He covers the vision, the implementation, and some lessons learned to illustrate what it takes to build new AI technology that 100 million people will care about.
Slides for talk given at Women in Engineering on March 20, 2021.
Abstract:
Natural language understanding is a fundamental task in artificial intelligence. English understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Discovery. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in the past few years in addressing these challenges. We will also showcase how universal semantic representation of natural languages can enable cross-lingual information extraction in concrete domains (e.g. compliance) and show ongoing efforts towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Towards Universal Semantic Understanding of Natural LanguagesYunyao Li
Keynote talk at TextXD 2019(https://www.textxd.org)
Abstract:
Understanding the semantics of the natural language is a fundamental task in artificial intelligence. English semantic understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Compare and Comply. However, scaling existing products/services to support additional languages remain an open challenge. In this demo, we will present Polyglot, a multilingual semantic parser capable of semantically parsing sentences in 9 different languages from 4 different language groups into the same unified semantic representation. We will also showcase how such universal semantic understanding of natural languages can enable cross-lingual information extraction in concrete domains (e.g. insurance and compliance) and show promise towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Omar Tawakol at AI Frontiers: The Rise Of Voice-Activated Assistants In The W...AI Frontiers
The market is already demonstrating strong value in the home for voice-activated AI, but the work environment is yet to catch up. Omar will explain why voice-activated AI is the most important development to come to the workplace. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable, and dive specifically into the challenges of ASR (Automatic Speech Recognition), NLP and neural networks in creating these kinds of voice-activated assistants. He will share how his team have overcome these challenges.
"erlang, webmail and hibari" at Rakuten tech talkCLOUDIAN KK
Presentation materials to talk about erlang overview, webmail development by erlang and "hibari" use case for GB mail box web mail at Rakuten tech talk on August 24, 2010
Deep Learning in NLP (BERT, ERNIE and REFORMER)Biswajit Biswas
in this session we can learn a brief journey of NLP computing with focus in deep learning. Three key things - RNN, Word Embedding and Attention is called upon for illustrating the success of Transformer models
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
This is a survey about Dialog System, Question and Answering, including the 03 generations: (1) Symbolic Rule/Template Based QA; (2) Data Driven, Learning; (3) Data-Driven Deep Learning. It also presents the available Frameworks and Datas for Dialog Systems.
AI is New Electricity and Deep Learning is one of key enablers for this, it breaks known limits of possible and disrupts vast areas of our modern life. From dev standpoint it sounds science-heavy and requires PhD in fact its not. Here I’m going to explain why in theory and practice with Kotlin.
65 - An Empirical Simulation-based Study of Real-Time Speech Translation for ...ESEM 2014
Context: Real-time speech translation technology is today available but still lacks a complete understanding of how such technology may affect communication in global software projects. Goal: To investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements.
Method: We performed an empirical simulation-based study including: Google Web Speech API and Google Translate service, two groups of four subjects, speaking Italian and Brazilian Portuguese, and a test set of 60 technical and non-technical utterances.
Results: Our findings revealed that, overall: (i) a satisfactory accuracy in terms of speech recognition was achieved, although significantly affected by speaker and utterance differences; (ii) adequate translations tend to follow accurate transcripts, meaning that speech recognition is the most critical part for speech translation technology.
Conclusions: Results provide a positive albeit initial evidence towards the possibility to use speech translation technologies to help globally distributed team members to communicate in their native languages.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
As a data science Intern at Leapcheck Services private limited, I have developed a naive chatbot using sequence to sequence model by LSTM of RNN. Sharing the tutorial which I made explicitly for the deep learning enthusiasts to
provide them a basic insight on how chatbot can be developed with the help of recurrent neural network.
Panelists: Yoshiyasu Yamakawa (Intel), JP Barraza (Systran), Konstantin Dranch (Memsource), David Koot (TAUS)
The focus of this session will be on predictions and risk management. What kind of things can you predict and how can you manage risks by by analyzing your translation data or monitoring your productivity and quality. Tracking translation data in different cycles of the translation process (translation, post-editing, review, proof-reading) offers tremendous value when it comes to predicting future trends or making informed choices. What type of data can be valuable and what kind of predictions can we make using this data? How can we make more efficient use of already available data? How can we use this type of data to improve machine translation, automatic QA, error-recognition, sampling or quality estimation? How can academia and industry work together towards a common goal?
While academic research is more and more focusing on integration of deep learning approaches for machine translation, also called Neural Machine Translation, and shows promising and exciting results – the resulting systems still have important pragmatic limitations compared to the current generation of translation engine. We will be discussing how SYSTRAN is integrating these new techniques into production systems, the results and benefits for the end users, and our vision for the next versions.
Towards Universal Semantic Understanding of Natural LanguagesYunyao Li
Keynote talk at TextXD 2019(https://www.textxd.org)
Abstract:
Understanding the semantics of the natural language is a fundamental task in artificial intelligence. English semantic understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Compare and Comply. However, scaling existing products/services to support additional languages remain an open challenge. In this demo, we will present Polyglot, a multilingual semantic parser capable of semantically parsing sentences in 9 different languages from 4 different language groups into the same unified semantic representation. We will also showcase how such universal semantic understanding of natural languages can enable cross-lingual information extraction in concrete domains (e.g. insurance and compliance) and show promise towards seamless scaling existing NLP capabilities across languages with minimal efforts.
Omar Tawakol at AI Frontiers: The Rise Of Voice-Activated Assistants In The W...AI Frontiers
The market is already demonstrating strong value in the home for voice-activated AI, but the work environment is yet to catch up. Omar will explain why voice-activated AI is the most important development to come to the workplace. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable, and dive specifically into the challenges of ASR (Automatic Speech Recognition), NLP and neural networks in creating these kinds of voice-activated assistants. He will share how his team have overcome these challenges.
"erlang, webmail and hibari" at Rakuten tech talkCLOUDIAN KK
Presentation materials to talk about erlang overview, webmail development by erlang and "hibari" use case for GB mail box web mail at Rakuten tech talk on August 24, 2010
Deep Learning in NLP (BERT, ERNIE and REFORMER)Biswajit Biswas
in this session we can learn a brief journey of NLP computing with focus in deep learning. Three key things - RNN, Word Embedding and Attention is called upon for illustrating the success of Transformer models
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
This is a survey about Dialog System, Question and Answering, including the 03 generations: (1) Symbolic Rule/Template Based QA; (2) Data Driven, Learning; (3) Data-Driven Deep Learning. It also presents the available Frameworks and Datas for Dialog Systems.
AI is New Electricity and Deep Learning is one of key enablers for this, it breaks known limits of possible and disrupts vast areas of our modern life. From dev standpoint it sounds science-heavy and requires PhD in fact its not. Here I’m going to explain why in theory and practice with Kotlin.
65 - An Empirical Simulation-based Study of Real-Time Speech Translation for ...ESEM 2014
Context: Real-time speech translation technology is today available but still lacks a complete understanding of how such technology may affect communication in global software projects. Goal: To investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements.
Method: We performed an empirical simulation-based study including: Google Web Speech API and Google Translate service, two groups of four subjects, speaking Italian and Brazilian Portuguese, and a test set of 60 technical and non-technical utterances.
Results: Our findings revealed that, overall: (i) a satisfactory accuracy in terms of speech recognition was achieved, although significantly affected by speaker and utterance differences; (ii) adequate translations tend to follow accurate transcripts, meaning that speech recognition is the most critical part for speech translation technology.
Conclusions: Results provide a positive albeit initial evidence towards the possibility to use speech translation technologies to help globally distributed team members to communicate in their native languages.
The NLP muppets revolution! @ Data Science London 2019
video: https://skillsmatter.com/skillscasts/13940-a-deep-dive-into-contextual-word-embeddings-and-understanding-what-nlp-models-learn
event: https://www.meetup.com/Data-Science-London/events/261483332/
As a data science Intern at Leapcheck Services private limited, I have developed a naive chatbot using sequence to sequence model by LSTM of RNN. Sharing the tutorial which I made explicitly for the deep learning enthusiasts to
provide them a basic insight on how chatbot can be developed with the help of recurrent neural network.
Panelists: Yoshiyasu Yamakawa (Intel), JP Barraza (Systran), Konstantin Dranch (Memsource), David Koot (TAUS)
The focus of this session will be on predictions and risk management. What kind of things can you predict and how can you manage risks by by analyzing your translation data or monitoring your productivity and quality. Tracking translation data in different cycles of the translation process (translation, post-editing, review, proof-reading) offers tremendous value when it comes to predicting future trends or making informed choices. What type of data can be valuable and what kind of predictions can we make using this data? How can we make more efficient use of already available data? How can we use this type of data to improve machine translation, automatic QA, error-recognition, sampling or quality estimation? How can academia and industry work together towards a common goal?
While academic research is more and more focusing on integration of deep learning approaches for machine translation, also called Neural Machine Translation, and shows promising and exciting results – the resulting systems still have important pragmatic limitations compared to the current generation of translation engine. We will be discussing how SYSTRAN is integrating these new techniques into production systems, the results and benefits for the end users, and our vision for the next versions.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
This slides covers introduction about machine translation, some technique using in MT such as example based MT and statistical MT, main challenge facing us in machine translation, and some examples of application using in MT
Machine translation is an easy tool for translating text from one language to another. You've probably used it. But do you know what machine translation really is? Or when you should or shouldn't use it? Navigate through this presentation to learn more!
Deep learning is one of the most exciting areas of machine learning and AI. This presentation covers all the very basics of deep neural networks, from the concept down to applications and why this technology is so popular in today's business landscape.
This presentation is provided by the Tesseract Academy, which provides executive education for deep technical subjects such as data science and blockchain. For a video of the presentation please visit https://www.youtube.com/watch?v=RiYGluH_cx0&t=0s&list=PLVce3C5Hi9BBfabvhEzYQTQDYEg2vtuxH&index=2
For an associated blog post about deep learning also visit http://thedatascientist.com/what-deep-learning-is-and-isnt/
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet"
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet
Neural networks have a long and rich history in automatic speech recognition. In this talk, we present a brief primer on the origin of deep learning in spoken language, and then explore today’s world of Alexa. Alexa is the AWS service that understands spoken language and powers Amazon Echo. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, and more. We also discuss the Alexa Skills Kit, which lets any developer teach Alexa new skills.
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Data Science, Machine Learning and Neural NetworksBICA Labs
Lecture briefly overviewing state of the art of Data Science, Machine Learning and Neural Networks. Covers main Artificial Intelligence technologies, Data Science algorithms, Neural network architectures and cloud computing facilities enabling the whole stack.
Deep Learning: Changing the Playing Field of Artificial Intelligence - MaRS G...MaRS Discovery District
Deep learning is changing the field of artificial intelligence and revolutionizing our online experience, with applications including speech and image recognition. Information and communications technology giants such as Google, Facebook, IBM and Baidu, among others, are rapidly deploying deep learning into new products and services.
Behind all of the present-day excitement about deep learning are years of high risk and hard work by a small group of eminent computer scientists and theorists connected through the Canadian Institute for Advanced Research (CIFAR).
PowerPoint Presentation given at Melbourne Cocoa Heads on an introduction to Machine Learning and the CoreML framework. Also included are references and sample MNIST_DRAW project.
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Similar to Deep Learning for Machine Translation, by Satoshi Enoue, SYSTRAN (20)
As contents published on the Internet are becoming more and more dominated by videos, requirements on the language translation have also changed. Specifically, video publishers and distributors have a significant interest in balancing both the translation time and the accuracy. To this end, Pactera has invested in solutions, which leverage machine translation to reduce the overall translation time, and recruit human translators to improve the accuracy in a Wikipedia-like fashion. At Pactera, we aim to help video contents to reach billions of people that were not possible before.
Review processes as the last step in quality assurance workflows are “notorious for causing delays and frustrations”. The reason normally is a flawed process: Many manual steps for the PMs, the lack of intuitive, layout-oriented collaboration software, plus the expectation of review to “fix a broken translation” in the last second rather than giving strategic process input. globalReview shifts this paradigm: As an integrated, collaborative platform with full layout editing it provides a positive review experience. At the same time, it pushes quality upstream applying DQF principles: Flexible content profiles define precise quality expectations; issue categories and scoring effectively gauge and also track translation quality over time; a sampling module allows for fast yet accurate quality evaluation. Put together, this allows the customer to raise the process from painful review to strategic quality management and gain valuable business intelligence.
A global P2P Trading Platform for TMs will be introduced. Tmxmall TM marketplace is the core, and client TM software and CATs are the input and output respectively. User of CATs is able to search the TMs of client users while it does not require client users to upload TMs to the cloud.
The presentation will introduce the NLP technologies used in Shiyibao and the main product features, covering the following points:
Function of giving automatic grades for translations based on translation quality automatic evaluation algorithm;
Function of giving automatic comments based on rules matching;
Function of sorting translations according to their similarity or some specific fragments to dramatically improve the efficiency of reviewing and commenting on translations.
In today’s digital economy, content is becoming smaller, more fragmented, and in need of on-demand translation in minutes and around the clock. Traditional localization models are no longer sufficient in meeting these always-on, agile, fast, and small translation requirements of the digital age. This is why mobile translation services like Stepes that are able to deliver quality, speed, and scalability are poised to see tremendous growth. During this 6-minute presentation, Stepes will demonstrate live its instant human translation service for micro content. Powered by human translators from around the world, Stepes is the world’s first mobile translation ecosystem delivering quality translation services using a networking model similar to Uber and Lyft.
For the language service industry, the biggest challenge is still, regardless if it’s for conventional language service mode or cloud-based service mode, translator resources. Using technology to help us map out the most suitable translators for each project is the key to ensure the high translation quality.
Computer Aided Translation Training System (CATS) provides a package solutions to the problems of translation translation. CATS combines artificial intelligence, data collection, and visualization of information technology, which makes the translation teaching, class management and monitoring on one single platform areality. Translation and interpretaton teaching resources on CATS are updated regularly into detailed categories, making the teaching materials easy to access. CATS supports translation and interpretation teaching and practices, company internships as well as scientific research.
Most of LSPs have not converted the translated bilingual documents to TM till now. Even the LSPs have established TMs, they are also confronted with disordered management of TMs and low efficiency. This report will share the way of quick TM establishment with Tmxmall Cloud-Based Smart Aligner, the way of Management of large-scale TMs with Private Cloud-Based TM for achieving pre-translation with large-scale TMs and team cooperation and etc.. Besides, the report will introduce Tmxmall TM marketplace, which is expected to promote TM sharing. Finally, we will share the experience of LSPs on alignment and Private Cloud-Based TM management for reducing translation costs and increasing profits.
SDL is the leader in global content management and language translation solutions. With more than 20 years of experience, SDL helps companies build relevant online experiences that deliver transformative business results on a global scale. Translation Industry continues to grow, and Freelancers, LSPs and Corporate clients all see increased demand as more and more content is created, so we have to address them all. As a Market-leading translation productivity tool, SDL Trados Studio is trusted by over 200,000 translation professionals to boost productivity, control quality and aid collaboration. SDL has launched Trados Studio 2017. This presentation will introduce SDL Trados Studio 2017 and highlight SDL’s new productivity booster- UPLIFT, which is well welcomed by global clients.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Deep Learning for Machine Translation, by Satoshi Enoue, SYSTRAN
1. Deep Learning for Machine
Translation
Satoshi Enoue, Jungi Kim, Jean Senellart, SYSTRAN
2. SYSTRAN Through Machine Translation
History
Rule Base Machine Translation
Example-Based Machine
Translation
Phrase Based Machine Translation
Syntax Based Machine Translation
Neural Machine
Translation
Hybrid Machine Translation
SYSTRAN
197
1968
SYSTRAN (SYStem
TRANslation)
founded by Dr.
Toma in La Jolla,
California (USA)
1969
Provided first
MT software for
the US Air Force,
(Russian to
English)
1975
Used by NASA
for the Apollo-
Soyuz
American-Soviet
project
1975
Translation systems for
all European languages
in the European
Commission
1986
SYSTRAN is acquired
by France’s Gachot SA,
thus becoming a
French company with
a U.S. subsidiary
1995
Pioneered development of
first Windows-based MT
software
1997
First free Web-based translation
service: Altavista Babelfish. SYSTRAN
made the Internet community aware
of the usefulness and capabilities of
machine translation
2002
SYSTRAN was used on
most major Internet
Portals: Yahoo!, Google,
AltaVista, Lycos.
1996
SYSTRAN within SEIKO’s
pocket translators.
1990’s
Port technology from mainframes to
Desktop PC’s and Client-Server environments
for personal and corporate use
2014
Following acquisition by CSLI,
SYSTRAN SA forms part of the
SYSTRAN International Group
2005
Launched embedded
translation software for
mobile devices
2009
Developed first
hybrid translation
software and
solution: SES 7
Translation Server
2011
Launch of SES 7
Training Server,
first solution for
self-learning of
MT engines
2015
SES8 Translation and Training
Server – Large Models
2016
More than 140 language Pairs.
Launch of SYSTRAN.io, the
Natural Language Processing
API platform
3. The new game changer
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Games
• Super Human Abilities
Sequence of fascinating results
and technologies over the last 3
years – all based on Deep Neural
Network (DNN) – covering a large
variety of domains…
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3
4. The new game changer
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Games
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4
5. The new game changer
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Games Abilities
• Google 2015 RNN voice search
recognition outperforms 2012
DNN models
• Baidu Deep Speech announces
16.5% improvement over
baseline and higher
performance than human in
noisy environment
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6. The new game changer - examples
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Gamesn Abilities
Les yeux prenaient des redoutables, des troncs de
feu.
Toutes les prétexticheurs par ces quatre
repentilleuses avec du sergent de Digne,
débragiffés nymoeurs sur les derniers instants à
hardis, boucher, sans dénongée en plus ennérence,
ils se refecturent encore. Ils auraient déjà mangé
ses très interses.
ShakespeareVictorHugo
Char-RNN, Andrej Karpathy, 2015
공급자는 AspNetXSprchyLibrary의 인스턴스를
만들어 다른 경고를 오버터 컴퓨터에 저장할 수
있습니다.
MSDN
20/04/2016
SYSTRAN - Copyright 2016
9. The new game changer - examples
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Games
20/04/2016 9word2vec, Google, 2013
10. The new game changer - examples
• Unified Neural Network
Architecture for several NLP tasks
POS tagging, chunking, NER, SRL
• Focus on avoiding task/linguistic
specific engineering
• Joint decision on the different tasks
Outperforms almost all of the state
of the art results for each individual
tasks
Natural Language Processing (Almost) from Scratch, Collobert et al., 201120/04/2016
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation
• … Gamesn Abilities
11. The new game changer - examples
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine Translation:
sentence encoding-decoding
• … Games
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, K. Cho et al, 2014
12. The new game changer - examples
• Deep Neural Network
Technologies
• Image Analysis
• Voice Recognition
• Text
• Text Generation
• Word Embeddings
• Multitask NLP
• Neural Machine : sentence encoding-
decoding
• … Games – DQN, AlphaGo
HUMAN-LEVEL CONTROL THROUGH DEEP REINFORCEMENT LEARNING, Google DeepMind, 201520/04/2016
14. The new game changer - examples
More and more evidence of
“super-human abilities”
Could we also reach Super-
human Machine Translation?
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15. The new game changer – ingredients
• MLP – multilayer perceptron
• Actually an “old concept”
• CNN
• Convolutional Neural network
• Word Embeddings
• Representing words as vectors
• RNN – GRU, LSTM
• MLP with memory
• Attention-Based models
• Ability to decide where to find
information
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16. The new game changer – ingredients
• MLP – multilayer perceptron
• Actually an “old concept”
• CNN
• Convolutional Neural network
• Word Embeddings
• Representing words as vectors
• RNN – GRU, LSTM
• MLP with memory
• Attention-Based models
• Ability to decide where to find
information
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17. The new game changer – ingredients
• MLP – multilayer perceptron
• Actually an “old concept”
• CNN
• Convolutional Neural network
• Word Embeddings
• Representing words as vectors
• RNN – GRU, LSTM
• MLP with memory
• Attention-Based models
• Ability to decide where to find
information
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18. The new game changer – ingredients
• MLP – multilayer perceptron
• Actually an “old concept”
• CNN
• Convolutional Neural network
• Word Embeddings
• Representing words as vectors
• RNN – GRU, LSTM
• MLP with memory
• Attention-Based models
• Ability to decide where to find
information
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19. The new game changer – ingredients
• MLP – multilayer perceptron
• Actually an “old concept”
• CNN
• Convolutional Neural network
• Word Embeddings
• Representing words as vectors
• RNN – GRU, LSTM
• MLP with memory
• Attention-Based models
• Ability to decide where to find
information
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All of these features are the ingredients to
Neural Machine Translation
20. About Neural Machine
Translation (NMT)
• The goal is to perform end-to-end translation
• Like in Speech Recognition
• The spirit is to remove all these features and have single system
• For Machine Translation – first NMT systems are encoder-decoder
• But not that magic
• Not systematic improvements over SMT baseline
• Use of ensemble systems
• Issues with sentence lengths, vocabulary size
• Solutions come back with some interest in “linguistic” characteristics
• Attention-Based model (alignment information)
• Deep Fusion with Language Model (better modelling of target language)
• Combine with word level (~ morphology)
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21. SYSTRAN approach to NMT
• Current Real Use-Case Requirements:
• Adaptation to (small) domain
• Help for post-editing
• Preserved speed
• Consistent results amongst multiple target languages
• Possibility to let users control translation through annotations, terminology
• …
• Toward Linguistically Motivated NN architecture
• SYSTRAN MT is composed of linguistic modules – let us start with them
• Lot of knowledge to leverage
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22. SYSTRAN Deep Learning Story – Part I
Language Identification
SYSTRAN LDK 1
•Statistical Classifier – 3-grams
•Heavily Feature Engineered over years
•e.g. diacritics model for latin language
•Include lexicon of frequent terms
•Quite good accuracy on news-type data
– need ~20 characters
Basic RNN
•“out-of-the-box” character level RNN
•no specific language specific
engineering
•80K words training per language
Google CLD
•Naïve Bayesian Classifier – 4-grams
•Trained on “big data”
•carefully scrapped over 100M pages
•Specific tricks for closely related
languages (Spanish/Portuguese)
•Geared for webpages - 200+ characters
Learnings: with same data RNN approach easily outperforms baseline, no
specific engineering needed… big data is not competing...
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News
Sentences
One-word
request
Ted-Talk
Sentences
Tweets
LDK 97 55.2 87.4 78.3
RNN 98.2 61.5 91.4 77.9
CLD 96.1 15.3 86 78.1
23. SYSTRAN Deep Learning Story – Part II
Part of Speech Tagging
Phase 1 - 1968-2014 - Handcrafting
•Manual Rule and Lexicon Coding of homography
•Closely related to Morphology description
•27 languages covered
Phase 2 - 2008-2015 – Annotating
•Train Classifier to "relearn” rules (fnTBL)
•Transfer knowledge through system output
•Maintenance through Annotation
Phase 3 - 2015- - Generalizing
•Relearn with RNN
•Joint decision (so far tokenization/part of speech
tagging) – working on morphology
•Better generalization from additional knowledge
(word embeddings)
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Learnings: Possibility to leverage ”handcrafting” and gain quality. But
learning becoming too smart – it also learns initial errors
24. SYSTRAN Deep Learning Story – Part III
Transliteration
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• Transliteration of person names
is depending on
• Source Language
• Target Language
• But also Name origin
• 카스파로프 = Kasparov
• 필리프 = Philippe
• Good Transliteration system
needs:
• Detection of origin
• Transliteration mechanism
•Extremely complicated – since it requires
phonetics modeling
Rule-Based
• Satisfactory but origin detection and multiple
domains
• No generalization - unseen sequence is wrong
PBMT
• Encoding-Decoding Approach
• Long distance "view" guarantee consistency of
transliteration
RNN
Learnings:
- losing reliability/traceability of the process
+ more global consistency, compactness of the solution
25. SYSTRAN Deep Learning Story – Part IV
Language Modeling
• RNN language model proves to overpass standard n-gram models
• No limitation in the span
• Seems to capture also better the language structure
• Better generalization due to word embedding
• Can be easily introduced in PBMT engine through rescoring
• Are still challenging pure sequence-to-sequence NMT approaches
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Learnings:
- Very long training process, several weeks of training for one language
+ Consistent quality gain, easy introduction in existing framework
26. Learnings from Deep Learning
• Consistent quality improvement in all the experiments/modules we
worked on
• Better leverage of existing training material
• Better generalization
• Incrementability: by design, it is immediate to feed more training data
– i.e. adapt dynamically to usage
• Globally more simple than alternative approaches and cognitively
interesting
• Fit to be combined in a global NN architecture
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28. What about Statistical Post Editing:
Learning to correct?
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• SPE was introduced as smart
alternative the SMT
• Corresponding to real MT use case for
localization
• Very little data can produce adaptation
• Reduce Human Post-Editor Work by
iteratively learning edits
• However implementation with PBMT
is not satisfactory
• PBMT does not learn to correct but to
translate
• Not incremental
• Learning to correct
• More control of the process
Toward a “translation checker”
• Change the paradigm – now human post-
editor to MT output, tomorrow
automatic post-editor to human output?
MT
HPE
29. Deep Learning for Machine Translation
• No doubt – it is coming:
• We will probably reach “superhuman” machine translation in coming years
• And this could become real translation assistant
• How is not yet completely clear
• From our perspective, we are working on hybrid approach = linguistically motivated
NN architecture
• More will also be coming from research world
• Still some work ahead
• Training of models is still a technological challenge
• We need the models to explain as much as to translate to become really useful – or
for language learning
• Multi-level analysis - document translation and not just sentences
• Multi-modal => could lead to full self language learning
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Editor's Notes
The last 3 years…
In Image recognition
In Voice Recognition
Show X is to Y what Z is to …
M
M
M
Road Sign Recognition
For some tasks
Actually it is not one single technology but a mix of different technologies – what is very seducing is this remains relatively simple, and appealing
Convolution Neural Network are very used in the image processing – and can be seen as consecutive layers of processing that progressively extract more and more advanced features
Actually it is not one single technology but a mix of different technologies – what is very seducing is this remains relatively simple, and appealing
Actually it is not one single technology but a mix of different technologies – what is very seducing is this remains relatively simple, and appealing
End-to-end – is also called “sequence-to-sequence”
Requirements from our customer are actually quite strong – and our goal is not to produce a generic academic NMT engine, but actual solutions for our customer requirements
So we would like to share with you findings of these moves to DNN and we took for that several modules
Example on Chinese
So we are not yet there – but what we foresee and work on is to establish a NN architecture preserving the actual traditional linguistic workflow with specialized NN stacking up to produce machine translation
From this specialization – we except several things - first we would be able to use the existing knowledge, second we would still have “checkpoints” in the process allowing to monitor translation process
Alternatively, the other important research directions for us – is to improve modeling on Statistical Post-Editing introduced in 2007 as an alternative to raising SMT. SPE is corresponding to real user-case: very little data, an existing system performing well but not really adapted to the task.