Machine Translation


Machine Learning Translation and the Google Translate Algorithm

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Modern machine translation systems use a different approach: they allocate the rules from text by analyzing a huge set of documents. Candidate 1: Statsbot makes it easy for companies to closely monitor data from various analytical platforms via natural language. Let's look at two human translations: Reference 1: Statsbot helps companies closely monitor their data from different analytical platforms via natural language. Reference 2: Statsbot allows companies to carefully monitor data from various analytics platforms by using natural language.


AI – The Present in the Making - Dataconomy

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But Professor Jon Oberlander disagrees. With a plethora of functions, Alexa quickly gained much popularity and fame. The next thing on Professor Jon Oberlander's list was labeling images on search engines. Over the years, machine translation has also gained popularity as numerous people around the world rely on these translators.


Google uses neural networks to translate without transcribing

@machinelearnbot

Google's latest take on machine translation could make it easier for people to communicate with those speaking a different language, by translating speech directly into text in a language they understand. The team trained its system on hundreds of hours of Spanish audio with corresponding English text. After a learning period, Google's system produced a better-quality English translation of Spanish speech than one that transcribed the speech into written Spanish first. And text translation service Google Translate already uses neural networks on its most popular language pairs, which lets it analyse entire sentences at once to figure out the best written translation.


Caffe2 adds RNN support.

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We are excited to share our recent work on supporting a recurrent neural network (RNN). The RNN engine supports the recycling of intermediate results across time steps and gives you the power to decide what to recycle. The static RNN engine supports all existing RNNCells and can be plugged in with almost no changes to the code. We follow the practice, common in machine translation, of using beam search at decoding time to improve our estimate of the highest-likelihood output sentence according to the model.


Facebook AI creates its own language? TechWire

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Facebook performs about 4.5 billion translations daily. The phrase-based system translated sentences word by word and usually made meaningless phrases. This system is capable of learning things by analyzing enormous amounts of data. Christopher Manning, a professor at Stanford University said that using the Facebook's neural system "You can have parallel computation on different parts of a sentence, You don't have to push things along word by word."


DeepL schools other online translators with clever machine learning

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As Frederic puts it: "Whereas Google Translate often goes for a very literal translation that misses some nuances and idioms (or gets the translation of these idioms dead wrong), DeepL often provides a more natural translation that comes closer to that of a trained translator." Linguee's co-founder, Gereon Frahling, used to work for Google Research but left in 2007 to pursue this new venture. In an email, Frahling told me that the time was ripe: "We have built a neural translation network that incorporates most of the latest developments, to which we added our own ideas." It's wasteful to go through the whole sentence only to find that the first word the network picked is wrong, and then start over with that knowledge, so DeepL and others in the machine learning field apply "attention mechanisms" that monitor for such potential trip-ups and resolve them before the CNN moves on to the next word or phrase.


DeepL Translator

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DeepL's networks consistently outperform other translation systems, making ours the world's best translation machine. Try it out for yourself or read on to see a quantitative comparison of our system to others. The gold standard for comparison of machine translation systems is the direct blind test. DeepL Translator, Google Translate, Microsoft Translator, and Facebook are fed 100 sentences to translate.


Salesforce is using AI to democratize SQL so anyone can query databases in natural language

@machinelearnbot

Their recent paper, Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning, builds on sequence to sequence models typically employed in machine translation. A reinforcement learning twist allowed the team to obtain promising results translating natural language database queries into SQL. "If I give a natural language question, there might be two or three ways to write the query. You can imagine how machine translation problems can quickly become massively complex with large vocabularies.


chaitanyamalaviya/lang-reps

@machinelearnbot

When a neural machine translation system learns to translate from one language to another, does it learn the syntax or semantics of the languages? Existing typological databases contain relatively full feature specifications for only a few hundred languages. Exploiting the existance of parallel texts in more than a thousand languages, we build a massive many-to-one NMT system from 1017 languages into English, and use this to predict information missing from typological databases. Experiments show that the proposed method is able to infer not only syntactic, but also phonological and phonetic inventory features, and improves over a baseline that has access to information about the languages' geographic and phylogenetic neighbors.


AI – The Present in the Making

#artificialintelligence

But Professor Jon Oberlander disagrees. With a plethora of functions, Alexa quickly gained much popularity and fame. The next thing on Professor Jon Oberlander's list was labeling images on search engines. Over the years, machine translation has also gained popularity as numerous people around the world rely on these translators.