{"id":112743,"date":"2021-04-30T17:06:26","date_gmt":"2021-04-30T17:06:26","guid":{"rendered":"https:\/\/www.trustedtranslations.com\/?p=112743"},"modified":"2023-11-20T18:06:32","modified_gmt":"2023-11-20T21:06:32","slug":"progres-technique-dans-la-traduction-automatique","status":"publish","type":"blog","link":"https:\/\/www.trustedtranslations.com\/fr\/blog\/progres-technique-dans-la-traduction-automatique","title":{"rendered":"Progr\u00e8s technique dans la traduction automatique"},"content":{"rendered":"

<\/p>

L\u2019un des plus importants d\u00e9veloppements dans le traitement automatique des langues a \u00e9t\u00e9 l\u2019expansion et l\u2019am\u00e9lioration des logiciels de traduction. Tandis qu\u2019auparavant c\u2019\u00e9tait le cas que les r\u00e9dacteurs devaient engager des professionnels pour traduire \u00e0 la main m\u00eame les documents les plus banals, ce qui co\u00fbtait cher, aujourd\u2019hui, il y a une vari\u00e9t\u00e9 de solutions num\u00e9riques pour la traduction.<\/p>

Voyons l\u2019\u00e9volution de la traduction automatique, d\u00e8s les logiciels de traitement de textes jusqu\u2019\u00e0 l\u2019intelligence artificielle.<\/p>

Ordinateurs personnels<\/strong><\/h3>

Dans les ann\u00e9es quatre-vingt, o\u00f9 l\u2019un des outils de travail les plus avanc\u00e9s disponibles \u00e0 nous \u00e9tait la machine \u00e0 \u00e9crire num\u00e9rique, les logiciels de traduction \u00e9taient rares et hautement sp\u00e9cifiques. Dans les grandes corporations, possiblement, on avait des logiciels pour ordinateurs centraux tels que Systran ou Logos, mais pour l\u2019utilisateur commun, \u00e0 la maison, le d\u00e9veloppement des logiciels de traduction \u00e9tait limit\u00e9 par la puissance de calcul requise pour imiter la parole et traduire des alphabets, des grammaires et des syntaxes compl\u00e8tement diff\u00e9rents.<\/p>

Les premiers pionniers furent les programmeurs d\u2019ordinateurs de cinqui\u00e8me g\u00e9n\u00e9ration au Japon qui ont d\u00e9velopp\u00e9 le calcul parall\u00e8le et la programmation logique, augmentant ainsi la capacit\u00e9 des ordinateurs de bureau. Ces d\u00e9veloppements ont \u00e9t\u00e9 inspir\u00e9s par la n\u00e9cessit\u00e9 de traduire entre l\u2019anglais et le japonais pour des fins commerciales. Entre les entreprises connues qui avaient besoin d\u2019un grand volume de traductions \u00e9taient Fujitsu, Mitsubishi, Toshiba et Hitachi.<\/p>

Intergraph et Systran ont commenc\u00e9 \u00e0 offrir syst\u00e8mes de traduction pour PC \u00e0 la fin de la d\u00e9cade. Et Babble Fish, le moteur de traduction en ligne fourni par Systran et h\u00e9berg\u00e9 par Alta Vista, a \u00e9t\u00e9 lanc\u00e9 en 1997.<\/p>

Trados<\/strong><\/h3>

En m\u00eame temps, en Allemagne, Jochen Hummel et Iko Knyphausen travaillaient ensemble, depuis 1984, sur un logiciel de traduction chez Trados. En 1992, ils ont lanc\u00e9 Multiterm, une base de donn\u00e9es de terminologie pour traducteurs. Comme la plupart des syst\u00e8mes qui existaient \u00e0 cette \u00e9poque, ce logiciel offrait une base de donn\u00e9es intelligente et non pas une analyse logique \u00e0 un haut niveau.<\/p>

En 1997, Trados GmbH a re\u00e7u un coup d\u2019encouragement de la part de Microsoft, qui avait d\u00e9cid\u00e9 d\u2019utiliser la technologie de Trados pour ses traductions internes. \u00c0 partir de ce moment, la compagnie a subi une croissance soutenue et, aujourd\u2019hui, elle offre Trados Studio a 270\u00a0000 professionnels de la traduction.<\/p>

Plate-formes pour les m\u00e9moires de traduction<\/strong><\/h3>

Le d\u00e9veloppement des syst\u00e8mes de gestion de traduction<\/a> a \u00e9t\u00e9 un autre grand bond en avant. En m\u00eame temps que ces syst\u00e8mes acc\u00e9l\u00e8rent les processus de traduction de documents formels tels que les documents juridiques, m\u00e9dicaux et financiers, ils cr\u00e9ent des bases de donn\u00e9es de termes fr\u00e9quents, lesquels ils affichent lorsqu’une expression similaire est utilis\u00e9e, garantissant ainsi la production de traductions exactes et \u00e9prouv\u00e9es.<\/p>

Tels syst\u00e8mes apprennent dans le sens de qu\u2019ils grandissent au pas de la comp\u00e9tence des utilisateurs, en enregistrant des phrases et termes qui ont \u00e9t\u00e9 utilis\u00e9s correctement auparavant. Mais ils ne traduisent pas les nuances de style ou les locutions idiomatiques.<\/p>

SDL Trados a incorpor\u00e9 rapidement les m\u00e9moires de traduction \u00e0 ses syst\u00e8mes de traduction assist\u00e9e par ordinateur (CAT, par son sigle en anglais)<\/a>. D\u2019autres fournisseurs cl\u00e9s de ce type de logiciel incluent WordFast (fond\u00e9 en 2006) et, dans le domaine d\u2019Internet, Google Translate, est devenu omnipr\u00e9sent (d\u2019autres plate-formes tels que Gengo et Ackuna sont \u00e9galement apparues).<\/p>

L’avantage de la migration de tels syst\u00e8mes vers Internet \u00e9tait, bien entendu, de s’appuyer sur des corpus toujours plus grands d\u2019unit\u00e9s de traduction, des ensembles de phrases reconnues comme des \u00e9quivalents fonctionnels dans plusieurs langues.<\/p>

Traduction automatique<\/strong><\/h3>

Avec l’av\u00e8nement de la traduction automatique (MT, par son sigle en anglais)<\/a>, une logique am\u00e9lior\u00e9e et un contexte sp\u00e9cifi\u00e9 ont \u00e9t\u00e9 ajout\u00e9s \u00e0 l’arsenal de techniques utilis\u00e9es pour obtenir une traduction fiable. Les substitutions de mots seraient adapt\u00e9es \u00e0 des milieux sp\u00e9cifiques et des ensembles de r\u00e8gles linguistiques toujours plus complexes pourraient \u00eatre appliqu\u00e9s pour garantir la production de traductions idiomatiques pr\u00e9cises.<\/p>

La traduction bas\u00e9e sur la grammaire, qui est relativement rigide et bas\u00e9e sur des r\u00e8gles, a \u00e9t\u00e9 progressivement remplac\u00e9e par un mod\u00e8le plus statistique. Cependant, un syst\u00e8me hybride qui combine les deux m\u00e9thodes pourrait produire des traductions toujours plus pr\u00e9cises.<\/p>

Moteurs d\u2019intelligence artificielle<\/strong><\/h3>

Dans le XXIe<\/sup> si\u00e8cle, les syst\u00e8mes de reconnaissance vocale, tels que l\u2019assistent virtuel d\u2019Apple, Siri, utilisent des algorithmes de reconnaissance vocale pour identifier la langue parl\u00e9e et pour la transformer en texte. Ceci est accompli apr\u00e8s un apprentissage de l\u2019idiolecte (usages propres \u00e0 un individu) de l\u2019utilisateur.<\/p>

Les syst\u00e8mes de traduction automatique neuronale (NMT, par son sigle en anglais) apprennent \u00e9galement \u00e0 partir de morceaux de texte, lesquelles ils int\u00e8grent \u00e0 travers des r\u00e9seaux neuronaux artificiels pour construire un mod\u00e8le statistique, \u00e0 partir duquel ils offrent des traductions toujours plus pr\u00e9cises. Ils cr\u00e9ent un r\u00e9seau neuronal virtuel qui est suffisamment intelligent pour fixer son regard sur diff\u00e9rentes parties d’une phrase afin de cr\u00e9er une image globale de son signifi\u00e9, plut\u00f4t que de traduire une unit\u00e9 \u00e0 la fois.<\/p>

Ces syst\u00e8mes cherchent \u00e0 comprendre \u00e0 la fois la grammaire d\u2019une langue et les usages stylistiques de leurs utilisateurs individuels. Google<\/a> est actuellement \u00e0 l\u2019avant-garde, bien qu’il y ait d’autres soci\u00e9t\u00e9s telles que Microsoft, Amazon et DeepL<\/a> en concurrence pour offrir les traductions les plus proches des traductions humaines, lesquelles peuvent ensuite \u00eatre raffin\u00e9es gr\u00e2ce au travail de post\u00e9dition<\/a> d’un linguiste humain.<\/p>

Dans les temps qui courent, o\u00f9 la reconnaissance vocale est r\u00e9pandue partout dans le monde et o\u00f9 il y a une forte demande de traductions entre les langues avec de grandes diff\u00e9rences d’orthographe (p. ex., anglais, mandarin, russe), les r\u00e9seaux neuronaux<\/a> d\u2019intelligence artificielle continueront \u00e0 \u00eatre un sujet de recherche important dans les prochaines d\u00e9cades.<\/p>

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