🎰 How good is Google translate? The most accurate language pairs

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Text translation: Translate between languages by typing β€’ Tap to Translate: Copy text in any app and tap the Google Translate icon to translate (all.


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Google Translate accuracy – why it’s such a mixed bag
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The Shallowness of Google Translate - The Atlantic
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good google translate

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Google Translate is: fast;; easy to use;; good at translating words separately;; satisfactory when translating short, common phrases;; very valuable.


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Google Translate is a free multilingual statistical and neural machine translation service Google Translate generates a translation proposal, it looks for patterns in hundreds of millions of documents to help decide on the best translation.


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By Google. View translations easily as you browse the web. By the Google Translate team. Highlight or right-click on a section of text and click.


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Google Translate (GT) is the world's number one translation software. It supports languages, 10 thousand language pairs, and processes.


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I would say it's really good compared to other languages that are less close to English and with a strongly different grammar (like Chinese, or, as I'm trying to.


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Google Translate is: fast;; easy to use;; good at translating words separately;; satisfactory when translating short, common phrases;; very valuable.


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Google Translate is a free multilingual statistical and neural machine translation service Google Translate generates a translation proposal, it looks for patterns in hundreds of millions of documents to help decide on the best translation.


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Translate. Download Google Translate and enjoy it on your iPhone, iPad and iPod touch. Google Translate 4+ Very nice. Good. djengafr, 16/01/


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But as the head of Google Translate, Macduff Hughes, told The Verge recently, machine learning is what makes Google's ever-useful translation.


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Related Posts. The neural model of machine translation relies on standard translation methods. Related articles Business writing: how to improve your translation skills The life of a translation project manager Globalizing your business? However, it is not true β€” GT simply bypasses the issue of language understanding. Il y a sa grosse voiture et sa petite voiture, ses pantoufles et ses pantoufles, ses livres et les siens. Dans leur maison, chaque membre de la famille a des objets personnels. A computer program has no memory, no imagination, and no understanding of the hidden meaning of words with which it can operate so quickly. It may even happen that a paragraph or two are translated perfectly, and this creates an illusion that GT understands the meaning of the text. Nevertheless, if neural translation works quite well with structurally similar languages, then with radically different language systems, for example, Japanese-Finnish, computer translation is noticeably inferior. Recent articles How being a creative worker can pay for your travels Website and social media translation for the Russian market How businesses can avoid productivity pitfalls 8 Everyday English words with Arabic origins Guaranteeing cybersecurity for remote workers. Considering the current level of technological development, any computer translator will require operating with complex language constructs, thus significantly reducing its speed. People understand such subtleties but GT only processes lines consisting of words and letters. In their home, each member of the family has personal belongings. There is his big car and her small car, his slippers and her slippers, and his books and hers. This can be described in one word β€” understanding. As you can see, the quality of translation in the English-Spanish and French-English pairs is very close to translation made by a human. It manipulated a set of answers to questions, thus creating an impression of intelligent phrases. The software simply ignored the most vital information in the sentence. This became possible due to the fact that AI began to use its own artificial language, which acts as an intermediate language in the translation process. Since then, the issue of whether a machine can think like a human has been called the Eliza effect. Three years have passed since then, and we can now evaluate its effectiveness. The most accurate language pairs by Michael 9 Sep, Machine Translation.

It supports languages, good google translate thousand language pairs, and processes about million translation requests every day.

Here is what GT came up with: Dans leur maison, chaque membre de la famille a des objets personnels. Sorry, your blog cannot share posts by email.

With its help, students will be able to locate and buy homework online a lot faster than they did in the past. In addition, a neural network can operate with letters the words are made up of. Instead, it operates on the semantics of the text and divides sentences up into dictionary segments. For example, if a system was trained to translate between English and Japanese , and English and Korean , then it can easily translate from Japanese to Korean without using English as an intermediate language. Back in the s, a mechanical device called Eliza was designed. By using special decoders, it determines the significance of each segment in the text. With other language pairs, the situation is not so good, but large-scale research into them is still ongoing. In a modern neural system, the smallest element is not a word but its fragments. Next, it computes the maximum number of possible meanings and translation options. As of today, GNMT uses about 32, such fragments. The maximum score is 6, while 0 is the minimum one. How good is Google translate? However, there is no reason to think that computer devices will not be able to think like humans in the future. The Eliza effect For any machine, computing device or software, words matter. It is clear to any person that the sentence is about a family in which each member has personal belongings. Below is a table in which the 6-point system for evaluating the quality of translation is used. The practical benefits of GT and similar technologies are hard to underestimate. For decades, software developers and AI researchers have been influenced by the Eliza effect. Due to the semantic and grammatical features of languages, proper translation requires completely different software algorithms which are implemented as separate modules and dictionaries in some programs. Did translation quality really improve and what else does it take to make it even better? This is not surprising since these language pairs were used for deep learning of GT algorithms. However, machines can never understand the in-depth meaning of these words. There is his big car and his little car, his slippers and his slippers, his books and his own. After all, modern technology is developing very fast. For any machine, computing device or software, words matter. If we use the French-English translator again, we get this sentence: In their home, each member of the family has personal belongings. The translation method implemented by Google developers is called zero-shot translation. Hence, we should expect rapid progress in the development of machine translation. This universal computer language, which was called Interlingua is absolutely unsuitable for communication between people. Before the advent of neural networks, translation was usually done in a word-for-word fashion. Based on an artificial neural network, it was meant to improve translation quality immensely. It quickly processes pieces of text without understanding their meaning. In , Google conducted large-scale surveys among regular GT users. This area of research is developing very rapidly, and such systems will become the primary means of automatic translation in the near future. Therefore, the quality of translation left much to be desired. So, we cannot say anything about the size of the car of each family member. GT did not understand the meaning and translated the sentence accordingly. The software translates the whole sentence by taking into account the context. However, we should not forget that GT cannot think like a human and is only able to processes texts in a certain way. Therefore, a translation system relying on advanced AI technology can be inaccurate and even erroneous. The self-learning feature of the system allows a neural network to accurately translate even slang, jargon, and neologisms that are not available in popular dictionaries. They were asked to evaluate three translation options: machine statistical, neural, and human ones. According to the developers, this approach enables to ensure high speed and accuracy of translation without consuming excessive computational power. However, something is still missing in this machine approach. In their house, every family member has personal things. The system simply translated separate words and phrases, taking into account basic grammar rules. A neural network can work with many pairs of languages, including those that were not involved in the initial learning process. Software developers have always tried to improve decryption methods or, in other words, attempted to cope with the translation task by using analytical powers of the machine without. Perhaps, they will even be able to do excellent translations between several different languages. The last step is to combine the translated segments with grammar. It is necessary when transliterating proper nouns from one ABC to another. Of course, GT can sometimes come up with sentences that sound pretty good. That is, computer translation is never focused on machine understanding. Most GT users assume that this software is able, at least sometimes, to understand the meaning of words. As a result, translation made by GT failed to convey the original meaning. Search Search for:. The results were impressive β€” translation that relied on neural networks turned out to be near perfect in some language pairs. It is a more sophisticated technology that relies on an intermediate artificial language. Facebook Twitter RSS. Therefore, GNMT developers tried to find a balance between accuracy and speed of translation.