What do Siri and machine translation have in common? They both produce strange, sometimes ridiculous language that leave us shaking our heads with confusion. Here at IVANNOVATION we frequently use Siri as well as Google's dictation function to get our work done. Siri instantly adds items to our to do lists, adds events to our calendars, and tells us answers to important questions like, "Siri, how much wood would a woodchuck chuck if a woodchuck could chuck wood?" (Ask Siri yourself.) Likewise, Google dictation helps us avoid the ruthless onslaught of carpal tunnel syndrome by typing up our articles and emails for us.

The go to place for quick and easy translations – Google Translate – just received a huge upgrade with Deep Learning algorithms boosting its translation capabilities and reducing errors by 60%. Google's experiments with neural machine translation pays off in a big manner. Like most translation services, Google Translate too relied on breaking down sentences into smaller phrases or groups of words and then translated these phrases which were later joined together to produce the output. With Neural Machine Translation, Google Translate can translate entire sentences without breaking them in phrases. This new approach has been said to reduce errors by at least 60 percent compared to the previous phrase based approach.

Googles engineers recently delivered a Google Translate upgrade that harnesses the popular artificial intelligence technique known as deep learning. Google has launched a Google Translate upgrade utilizing enhanced deep-learning techniques to produce more accurate translations. The neural machine translation system considers the entire sentence as one unit to be translated. The system relies on a recurrent neural network algorithm consisting of layered nodes, and a network of eight layers acts as the encoder and transforms the input into a list of vectors representing all possible meanings of each word. The second eight-layer network acts as the decoder and generates the translation one word at a time. Meanwhile, an attention network connects the encoder and decoder by directing the decoder to refer back to certain weighted vectors.

Using top-ranked documents in response to a query has been shown to be an effective approach to improve the quality of query translation in dictionary-based cross-language information retrieval. In this paper, we propose a new method for dictionary-based query translation based on dimension projection of embedded vectors from the pseudo-relevant documents in the source language to their equivalents in the target language. To this end, first we learn low-dimensional vectors of the words in the pseudo-relevant collections separately and then aim to find a query-dependent transformation matrix between the vectors of translation pairs appeared in the collections. At the next step, representation of each query term is projected to the target language and then, after using a softmax function, a query-dependent translation model is built. Finally, the model is used for query translation. Our experiments on four CLEF collections in French, Spanish, German, and Italian demonstrate that the proposed method outperforms a word embedding baseline based on bilingual shuffling and a further number of competitive baselines. The proposed method reaches up to 87% performance of machine translation (MT) in short queries and considerable improvements in verbose queries.

Google Translate has become a quick-and-dirty translation solution for millions of people worldwide since it debuted a decade ago. But Google's engineers have been quietly tweaking their machine translation service's algorithms behind the scenes. They recently delivered a huge Google Translate upgrade that harnesses the popular artificial intelligence technique known as deep learning. Machine translation services such as Google Translate have mostly used a "phrase-based" approach of breaking down sentences into words and phrases to be independently translated. But several years ago, Google began experimenting with a deep-learning technique, called neural machine translation, that can translate entire sentences without breaking them down into smaller components.

Have you ever been in a situation where knowing another language would have come in handy? I remember standing on the platform at Tokyo Station watching my train to Nagano -- the last train of the day -- pulling away without me on it. What ensued was a frustrating hour of gestures, confused smiles, and head-shaking as I wandered the station looking for someone who spoke English (my Japanese is unfortunately nonexistent). It would have been really helpful to have a bilingual pal along with me to translate. Bilingual pals can be hard to find, but Google's new translation software may be an equally useful alternative.

Google says that with certain languages, its new system -- dubbed Google Neural Machine Translation -- reduces errors by 60 percent. But the company plans to roll it out for the more than 10,000 language pairs now handled by Google Translate.

No one would accuse Google Translate, the favored tool of unscholarly high school language students everywhere, of being an inaccurate interpreter. The 10-year-old internet interpreter can fluently translate more than 100 tongues, recognize foreign restaurant menus and signage, and differentiate between dialects in real time. The project is called Google Neural Machine Translation, or GNMT, and it isn't strictly speaking new. It was first employed to improve the efficiency of single-sentence translations, explained Google engineers Quoc V. Le and Mike Schuster, and did so ingesting individual words and phrases before spitting out a translation. But the team discovered that the algorithm was just as effective at handling entire sentences -- even reducing errors by as much as 60 percent.

No one would accuse Google Translate, the favored tool of unscholarly high school language students everywhere, of being an inaccurate interpreter. The 10-year-old internet interpreter can fluently translate more than 100 tongues, recognize foreign restaurant menus and signage, and differentiate between dialects in real time. The project is called Google Neural Machine Translation, or GNMT, and it isn't strictly speaking new. It was first employed to improve the efficiency of single-sentence translations, explained Google engineers Quoc V. Le and Mike Schuster, and did so ingesting individual words and phrases before spitting out a translation. But the team discovered that the algorithm was just as effective at handling entire sentences -- even reducing errors by as much as 60 percent.

Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there are just so many words, phrases and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. Google has been working on a machine learning translation technique for years, and today is its official debut. The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods. Here's how things have evolved (in a nutshell). A very simple technique for translating -- one a kid or simple computer could do -- would be to simply look up each word encountered and switch it with the equivalent word in another language.