* 3Machine translation: Beyond Babel
   Computers have got much better at translation, voice recognition and
   Speech recognition has made remarkable advances. Machine translation,
   automatic translation between languages within a few years.
   machine translation and automatic speech recognition, it concluded that
   translation—got stuck in a conceptual cul-de-sac: the rules-based
   approach. In translation, this meant trying to write rules to analyse
   machine translation, the software scans millions of words already
   inappropriate translations offered by online tools like BabelFish began
Machine translation: Beyond Babel
   Computer translations have got strikingly better, but still need human
   talk. High-quality automated translation seems even more magical than
   The idea has been around since the 1950s, and computerised translation
   is still known by the quaint moniker “machine translation” (MT). It
   automated translations, including that of “Mi pyeryedayem mislyi
   provided rough translations, mostly to America’s armed forces.
   different translations for “pen”: any pen big enough to hold a box
   translation. Statistical, “phrase-based” machine translation, like
   The phrase-based approach would ensure that the translation of a word
   train its translation engines; in 2007 it switched from a rules-based
   text that seemed to be a translation of another—for example, pages
   corpus) creates a “translation model” that generates not one but a
   series of possible translations in the target language. The next step
   of monolingual training data are not.) As with the translation model,
   from the training data, then ranks the outputs from the translation
   Statistical machine translation rekindled optimism in the field.
   and structurally quite different, make accurate translation harder than
   more accurate than before. Translations between English and (say)
   Neural-network-based translation actually uses two networks. One is an
   neural-net translation with the phrase-based kind. The latter, he says,
   word-for-word translation, once again taking account of the immediately
   Neural-network translation requires heavy-duty computing power, both
   Smaller translation companies and researchers usually rent this kind of
   Fully automated, high-quality machine translation is still a long way
   off. For now, several problems remain. All current machine translations
   proceed sentence by sentence. If the translation of such a sentence
   parallel texts available on which to train a translation engine. So a
   system that claims to offer such translation is in fact usually running
   two translations rather than one, multiplying the chance of errors.
   Even if machine translation is not yet perfect, technology can already
   help humans translate much more quickly and accurately. “Translation
   translation, for instance, can train the system on medical translations
   spew out rough but near-instantaneous speech-to-speech translations.
   Translation management has also benefited from innovation, with clever
   translation memory, customisation by the individual translator and so
   on. Translation-management software aims to cut out the agencies that
   favourite, says that in future translation customers will choose how
   much human intervention is needed for a translation. A quick automated
   Translation software will go on getting better. Not only will engineers
   for a translation, but an easy-to-use interface allows the translator
   the corrections are fed back into the translation engine, which learns
   translations in that specialist field.
   translation industry saying that “in the past few years the translation
   less-than-perfect translation will be good enough.
   translation have something in common: there are huge stores of data
   for translation) that can be used to train machines. But there are no