Google launches neuromachine translation system or breaks global language barriers

Abstract On September 28th, Google released the Neural Machine Translation (GNMT: GoogleNeuralMachineTranslation) system, which uses the most advanced training technology to greatly improve the level of machine translation...
On September 28th, Google released the GNMT (Google Neural Machine Translation) system, which uses the most advanced training technology to significantly improve the machine translation level and reduce the translation error by 55%-85%.
Ten years ago, Google released Google Translate. The core algorithm behind this service is phrase-based machine translation, which breaks down input sentences into words and phrases and then translates them independently. Neural machine translation uses the input sentences as A global translation, Google Neuro Machine Translation represents a major milestone.
At present, the Google Neuro Machine Translation System has been put into Chinese-English translation. Now, the Chinese and English translations of Google Translate for mobile and web versions are fully using neuromachine translation (about 18 million translations per day). Chinese to English translation is one of more than 10,000 language pairs supported by Google Translate, and in the coming months, Google will continue to extend the neural translation system to more languages.
China's super-hard material network Xiaobian passed the overnight test, the impression is: the effect of the group. Compared with the previous, the error rate has been reduced by more than half. There are still obvious grammatical errors, and a few proper nouns are not translated correctly. But overall, Google has approached the major issue of translation. The following is a translation example. It can be seen that the structural problem is removed. The dialect of Figure 1 fails. The proverb of Figure 2 thinks that Keep calm and get rich is better. Figure 3 shows that the colloquial short sentences are flawed. The last three groups of idioms and poems And proper noun translation errors. But overall, the progress is still huge.





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