Fichier de travail (INPUT) : ./DUMP-TEXT/2-8.txt
Encodage utilisé (INPUT) : utf-8
Forme recherchée : translation|traduction|机器
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Ligne n°68 : ... Bel-Air” and the theme song from Moana to see what funny or ridiculous- Ligne n°69 : translations Google would generate. While the tool isn’t nearly as bad
Ligne n°70 : as videos make it out to be, this negative PR has kept companies from ...- Ligne n°74 : But Google Translate doesn’t represent all machine translation.
Ligne n°75 : However, it is a brand that happens to be well-known and free. And, in ...
Ligne n°75 : ... However, it is a brand that happens to be well-known and free. And, in- Ligne n°76 : translation — just like everything else — you get what you pay for.
- Ligne n°82 : Professional (not free) machine translation helps companies translate
Ligne n°83 : more content more quickly and at a lower price point. And when a human ...
Ligne n°84 : ... reviewer checks the work, quality is found to be just as high as that- Ligne n°85 : of traditional translation. The language industry calls this pairing:
Ligne n°86 : machine translation post-editing, or MT+PE for short. Traditionally, ...
Ligne n°85 : ... of traditional translation. The language industry calls this pairing:- Ligne n°86 : machine translation post-editing, or MT+PE for short. Traditionally,
Ligne n°87 : when you buy translation, your content goes through two rounds — the ...
Ligne n°86 : ... machine translation post-editing, or MT+PE for short. Traditionally,- Ligne n°87 : when you buy translation, your content goes through two rounds — the
Ligne n°88 : first person converts it into the new language then the second checks ...
Ligne n°89 : ... for error. With MT+PE, the computer takes the first pass, speeding up- Ligne n°90 : the entire process. This is what makes machine translation okay for
Ligne n°91 : certain marketing material, but not anything too highly-nuanced, Rick ...
Ligne n°92 : ... Antezana with the Association of Language Companies cautions. “Paid- Ligne n°93 : machine translation should be used when translating content of high
Ligne n°94 : volume and low risk/low importance,” he says. ...
Ligne n°96 : ... It all depends on the training. Words are stored in what computer- Ligne n°97 : programmers call an engine. Machine translation engines must be
Ligne n°98 : specifically trained for the old and new languages, as well as the ...
Ligne n°99 : ... subject matter you need. As with all artificial intelligence or machine- Ligne n°100 : learning, machine translation needs data to improve. If your company’s
Ligne n°101 : tech team uses the engine to translate user questions, it’ll be good at ...
Ligne n°110 : ... Globalization and Localization Association, says, “The big development- Ligne n°111 : these days is neural machine translation, which is blowing other
Ligne n°112 : machine translation out of the water.” Using neural network technology ...
Ligne n°111 : ... these days is neural machine translation, which is blowing other- Ligne n°112 : machine translation out of the water.” Using neural network technology
Ligne n°113 : — a type of machine learning designed to mimic neurons in the human ...
Ligne n°113 : ... — a type of machine learning designed to mimic neurons in the human- Ligne n°114 : brain, neural machine translation can train in multiple languages and
Ligne n°115 : directions at once. Separate engines are no longer needed, saving your ...
Ligne n°120 : ... — whether it be an enterprise-level, global company or a small software- Ligne n°121 : company — using machine translation to represent any kind of
Ligne n°122 : content, including marketing content like social is a big gamble, as ...
Ligne n°123 : ... the software is incredibly well developed, but never perfect,” Antezana- Ligne n°124 : says. “The fewer eyeballs on potential content for translation and the
Ligne n°125 : higher the volume, the more appropriate it would be.” ...
Ligne n°147 : ... Tags cxm, digital marketing, dxm, machine learning, machine- Ligne n°148 : translation, marketing, terena bell