"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
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.
In this world, the bot-to-bot business model will be something ordinary and it is going to be populated by two types of bots: master bots and follower bots. This would result in some players creating "universal" bots (master bots) which everyone else will use as gateways for their (peripheral) interfaces and applications. Google has recently created a "Neural Machine Translation", a relevant leap ahead in the field, with the new version even enabling zero-short translation (in languages which they were not trained for). This was not originally intended to part of this article, but I found useful to go quickly through main players in the space in order to understand the importance of speech recognition in business contexts.
From a 30,000-foot view, all we really need to know is that it adds to Google's capabilities an incredible speed of data accumulation, interpretation, and reaction. One of the easiest examples of an area machine learning can greatly increase Google's capabilities is in links. Previously, Google engineers would have to create lists of poor-quality sites and block their link juice flow manually, program specific characteristics of a bad link based on what they'd seen prior, or set up devaluation functions into link calculations and hope that it didn't include too many false positives. We're talking about machine learning and Google's ever-increasing capability to understand the world around us, as well as our own personal needs and wants.
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. The new AI system, processed by neural networks, translates entire sentences in one go. According to Facebook, the new system will increase BLEU, a metric judging machine translation accuracy, by 11 percent across all languages compared with the phrase-based systems. Facebook's rivals Google and Microsoft have also been working on neural machine translation.
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. 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. "To remedy this and build our neural network systems, we started with a type of recurrent neural network known as sequence-to-sequence LSTM (long short-term memory) with attention," software engineers Necip Fazil Ayan, Juan Miguel Pino, and Alexander Sidorov, members of Facebook's Applied Machine Learning team, said in a blog post.
The social network's AI research team have turned translation services over to AI completely, it said in a post from an official blog. Facebook's Applied Machine Learning team has been training its AI to better understand how things like slang, typos, and intent work, in order to provide more accurate translations. The essential difference, for those of us who aren't machine-learning programmers, is that the latter can only process things in order. Facebook's Applied Machine Learning team makes communication with people who don't speak the same languages seamless, effortless, and integrated into Facebook.
But most people don't actually care how the engine of machine learning translation works. To learn more about structure and mathematical models of LSTM, you can read the great article "Understanding LSTM Networks." Our next step is bidirectional recurrent neural networks (BRNNs). If you want to go deeper with that, take a look at the article Google's Neural Machine Translation System.
Previously, the social networking site used simpler phrase-based machine translation models, but it's now switched to the more advanced method. While the phrase-based system translated sentences word by word, or by looking at short phrases, the neural networks consider whole sentences at a time. "With the new system, we saw an average relative increase of 11 percent in BLEU -- a widely used metric for judging the accuracy of machine translation -- across all languages compared with the phrase-based systems," the company said. When a word in a sentence doesn't have a direct corresponding translation in a target language, the neural system will generate a placeholder for the unknown word.
To continue improving the quality of our translations, we recently switched from using phrase-based machine translation models to neural networks to power all of our backend translation systems, which account for more than 2,000 translation directions and 4.5 billion translations each day. These new models provide more accurate and fluent translations, improving people's experience consuming Facebook content that is not written in their preferred language. To remedy this and build our neural network systems, we started with a type of recurrent neural network known as sequence-to-sequence LSTM (long short-term memory) with attention. These quality improvements make CNNs an exciting new development path, and we will continue our work to utilize CNNs for more translation systems.
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. For example, a translation from English to French is one direction, French to English is a second, and French to Italian is a third direction, and so forth.