This course teaches the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment, and helps them understand what the modern language service industry looks like. This course covers introduction to modern language services industry, basic principles and concepts of translation technology, information technology used in the process of language translation, how to use electronic dictionaries, Internet resources and corpus tools, practice of different computer-aided translation tools, translation quality assessment, basic concepts of machine translation, globalization, localization and so on. As a compulsory course for students majoring in Translation and Interpreting, this course is also suitable for students with or without language major background. By learning this course, students can better understand modern language service industry and their work efficiency will be improved for them to better deliver translation service.

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

When thinking about the rise of machine learning as it relates to SEO, we can be faced with a frightening scenario depending on the type of SEO you are. SEOs, like myself, who are logic-based and have historically worked relying on an understanding of the signals at play and how they fluctuate may be chewing their nails more than the SEOs who have relied more on the creative side. Where I once used to scratch my head wondering how the "build great content and they will come" approach was even conceivable, SEOs who carry out that approach are the ones who are likely less worried today. And they should be…sort of. Before we dive into what's changing let's first answer the question: We're not going to get into a big lesson around all that is machine learning here or we won't have time to actually cover how it impacts us and what our future SEO strategy needs to look like.

Facebook announced on Thursday it is improving its 4.5 billion daily translations with an artificial intelligence powered system. The social media giant supports over 45 languages for its two billion users worldwide, which makes translating content common on the platform. To do so, one simply clicks on the "see translation" button below a post or a comment. But despite how easy the process is... one may not be always satisfied with the accuracy of the translation. The previous system linked to the button, according to Facebook, is phrase-based and translates words or short phrases one at a time, missing the grammar and word orders.

Facebook announced today that it has started using neural network systems to carry out more than 4.5 billion translations that occur each day on the backend of the social network. Translations carried out with recurrent neural networks (RNNs) were able to scale with the use of Caffe2, a deep learning framework open-sourced by Facebook in April. The Caffe2 team today also announced that in part due to work done around translation, the framework is now able to work with recurrent neural networks. "Using Caffe2, we significantly improved the efficiency and quality of machine translation systems at Facebook. We got an efficiency boost of 2.5x, which allows us to deploy neural machine translation models into production," the Caffe2 team said in a blog post.

Now, we don't need to struggle so much– we can translate phrases, sentences, and even large texts just by putting them in Google Translate. This post is for those who do care. If the Google Translate engine tried to kept the translations for even short sentences, it wouldn't work because of the huge number of possible variations. The best idea can be to teach the computer sets of grammar rules and translate the sentences according to them. If only it were as easy as it sounds.

Every day, Facebook performs some 4.5 billion automatic translations -- and as of yesterday, they're all processed using neural networks. Previously, the social networking site used simpler phrase-based machine translation models, but it's now switched to the more advanced method. "Creating seamless, highly accurate translation experiences for the 2 billion people who use Facebook is difficult," explained the company in a blog post. "We need to account for context, slang, typos, abbreviations, and intent simultaneously." The big difference between the old system and the new one is the attention span.

While EMMA is a relatively simple application, developers are thinking bigger as well: Today's cognitive technologies can track the course, speed, and destination of nearly 2,000 airliners at a time, allowing them to fly safely.4 Over time, AI will spawn massive changes in the public sector, transforming how government employees get work done. It's likely to eliminate some jobs, lead to the redesign of countless others, and create entirely new professions.5 In the near term, our analysis suggests, large government job losses are unlikely. But cognitive technologies will change the nature of many jobs--both what gets done and how workers go about doing it--freeing up to one quarter of many workers' time to focus on other activities.

Language translation is one of the ways we can give people the power to build community and bring the world closer together. It can help people connect with family members who live overseas, or better understand the perspective of someone who speaks a different language. We use machine translation to translate text in posts and comments automatically, in order to break language barriers and allow people around the world to communicate with each other. Creating seamless, highly accurate translation experiences for the 2 billion people who use Facebook is difficult. We need to account for context, slang, typos, abbreviations, and intent simultaneously.

Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a "phrase-based machine translation" technique that wasn't powered by neural networks. That system translated words or small groups of words individually, and didn't do a good job of considering the context or word order of the sentence. As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it. The first Turkish-to-English sentence reads this way: "Their, Izmir's why you said no we don't expect them to understand." Now check out the newer translation: "We don't expect them to understand why Izmir said no." Notice how the AI fixed the mistakes in word and phrase order?