{"id":6656,"date":"2021-06-29T00:06:08","date_gmt":"2021-06-28T22:06:08","guid":{"rendered":"https:\/\/www.ibidemgroup.com\/edu\/?p=6656"},"modified":"2024-03-04T12:37:50","modified_gmt":"2024-03-04T11:37:50","slug":"introduccion-machine-translation-mt","status":"publish","type":"post","link":"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/","title":{"rendered":"\ufeffLost in Machine Translation: una peque\u00f1a introducci\u00f3n a la MT"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\" style=\"text-align:center\"><span style=\"color:#0693e3\" class=\"color\">Traducci\u00f3n realizada por Jose,<\/span><a href=\"https:\/\/www.ibidemgroup.com\"><span style=\"color:#0693e3\" class=\"color\"> traductor especializado en Machine Translation<\/span><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"text-align:center\"><strong><span style=\"color:#0693e3\" class=\"color\">Texto original escrito por Justin Houghton, publicado el 03\/03\/2020 en <\/span><\/strong><span style=\"color:#0693e3\" class=\"color\">dev.to: <\/span><a href=\"https:\/\/dev.to\/justinhtn\/lost-in-machine-translation-4edl\"><span style=\"color:#0693e3\" class=\"color\">https:\/\/dev.to\/justinhtn\/lost-in-machine-translation-4edl<\/span><\/a><\/p>\n\n\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\">***<\/p>\n\n\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">La Traducci\u00f3n Autom\u00e1tica o MT (Machine Translation) es el campo de estudio que se ocupa de utilizar programas inform\u00e1ticos para traducir texto o voz de un idioma a otro. A un nivel (muy) bajo, la MT realiza la traducci\u00f3n palabra por palabra. Sin embargo, si alguna vez has intentado aprender un nuevo idioma, sabr\u00e1s que la traducci\u00f3n palabra por palabra por s\u00ed sola no puede proporcionar una gran traducci\u00f3n: necesitas comprender frases completas para poder definir con mayor precisi\u00f3n una palabra en su contexto.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Hay quienes afirman que la ambig\u00fcedad de las palabras impide, por definici\u00f3n, que la MT sea capaz de distinguir dos significados de la misma palabra. Sin embargo, los investigadores est\u00e1n trabajando intensamente en el desarrollo de nuevos sistemas destinados a abordar este problema, incluyendo la MT profunda y superficial, que se tratar\u00e1 en un post posterior. Para esta introducci\u00f3n, nos centraremos en ofrecer una r\u00e1pida visi\u00f3n general de 5 subcampos de la MT que, aunque son v\u00edctimas de los problemas de ambig\u00fcedad y contexto a los que se enfrenta la MT en la actualidad, a\u00fan consiguen proporcionar traducciones lo suficientemente precisas como para revolucionar la comunicaci\u00f3n.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" src=\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--HyboHze2--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/miro.medium.com\/max\/955\/0%2AmE78Pan6jZMIxFmf\" alt=\"Example\"\/><\/figure><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--HyboHze2--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/miro.medium.com\/max\/955\/0%2AmE78Pan6jZMIxFmf\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Como\nsubcampo de la ling\u00fc\u00edstica computacional y el procesamiento del lenguaje\nnatural, la MT suele agruparse en 5 \u00e1reas distintas.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>MT basada en reglas<\/li><li>MT estad\u00edstica<\/li><li>MT basada en ejemplos<\/li><li>MT h\u00edbrida<\/li><li>MT neuronal<\/li><\/ul>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">INDICE<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/#MT_basada_en_reglas\" >MT basada en reglas<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/#MT_estadistica\" >MT estad\u00edstica<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/#MT_basada_en_ejemplos\" >MT basada en ejemplos<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/#MT_hibrida\" >MT h\u00edbrida<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/#MT_neuronal\" >MT neuronal<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MT_basada_en_reglas\"><\/span>MT basada en reglas <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A diferencia\nde otros enfoques de MT, la MT basada en reglas utiliza reglas sint\u00e1cticas y\nmorfol\u00f3gicas para ayudar a la traducci\u00f3n. Los sistemas basados en reglas\nvinculan una frase de entrada con la estructura de la frase de salida, lo que\nayuda a preservar su significado.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">En su forma\nm\u00e1s simple, la MT basada en reglas requiere 3 cosas distintas.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Un diccionario que puede relacionar      cada palabra de una lengua determinada con la lengua de salida deseada.<\/li><li>Reglas gramaticales y sint\u00e1cticas de la lengua de entrada.<\/li><li>Reglas gramaticales y sint\u00e1cticas para la lengua de salida deseada.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Algunas\nventajas notables de este tipo de MT son que no se necesitan textos biling\u00fces,\nque es independiente del dominio general y que permite un control total sobre\nla depuraci\u00f3n y la adici\u00f3n de nuevas reglas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MT_estadistica\"><\/span>MT estad\u00edstica <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Como su\nnombre indica, el ML estad\u00edstico trata de generar traducciones utilizando\nm\u00e9todos estad\u00edsticos basados en textos biling\u00fces. Google Translate, uno de los\nservicios de traducci\u00f3n m\u00e1s conocidos, cambi\u00f3 a la traducci\u00f3n estad\u00edstica en\n2007 desde SYSTRAN, que hab\u00eda estado utilizando en a\u00f1os anteriores. Este tipo\nde traducciones funciona detectando patrones en millones de documentos que ya\nhan sido traducidos por humanos y utilizando esos patrones para hacer\npredicciones basadas en sus resultados.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Un claro e\nimportante inconveniente de la MT estad\u00edstica es que depende de enormes\ncantidades de texto ya traducido. Aunque hay enfoques m\u00e1s recientes, como <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10590-008-9048-z\">METIS 2<\/a>, que se centran en la b\u00fasqueda de\npatrones en la estructura sint\u00e1ctica, lo que permite reducir el tama\u00f1o del\ncorpus, es seguro asumir ahora mismo que cuantos m\u00e1s textos traducidos tenga\nuna lengua concreta, m\u00e1s precisa ser\u00e1 cualquier nueva traducci\u00f3n.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MT_basada_en_ejemplos\"><\/span>MT basada en ejemplos <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Al igual que\nla MT estad\u00edstica, la MT basada en ejemplos se basa en la idea de una analog\u00eda\ny requiere el uso de textos ya traducidos en su corpus.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Dada una\nfrase concreta (A) que necesita ser traducida, se seleccionan diferentes frases\n(B y C) del corpus que tienen subcomponentes similares. Los subcomponentes de B\ny C se utilizan para traducir los subcomponentes de A, que al unirse forman una\nnueva frase traducida.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--15esD4c2--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/\/miro.medium.com\/max\/1822\/0%2ALIYMwimIKBMuQ1sa\" alt=\"Example\"\/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/res.cloudinary.com\/practicaldev\/image\/fetch\/s--15esD4c2--\/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880\/https:\/miro.medium.com\/max\/1822\/0%2ALIYMwimIKBMuQ1sa\"><\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MT_hibrida\"><\/span>MT h\u00edbrida <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">La MT\nh\u00edbrida pretende aprovechar los puntos fuertes tanto de la MT basada en reglas\ncomo de la estad\u00edstica. Se han propuesto varios enfoques sobre c\u00f3mo construir\nun sistema de MT h\u00edbrido, pero en general hay dos formas distintas de\nutilizarlo en la pr\u00e1ctica.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Reglas post-procesadas por las estad\u00edsticas.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Al igual que\nen una traducci\u00f3n basada en reglas, en este primer ejemplo de MT h\u00edbrida, las\ntraducciones las realiza un motor basado en reglas. La diferencia es que las\nestad\u00edsticas se utilizan para el posprocesamiento del resultado en un esfuerzo\npor refinar el resultado de la traducci\u00f3n. En este caso, las estad\u00edsticas\nayudan a corregir el resultado de un motor basado en reglas.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Estad\u00edsticas guiadas por las reglas sint\u00e1cticas y metam\u00f3rficas.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Las reglas\nse utilizan en el preproceso para ayudar a guiar la traducci\u00f3n estad\u00edstica, y\ntambi\u00e9n se utilizan en el postproceso para ayudar a ajustar\/refinar el\nresultado de la traducci\u00f3n. En este ejemplo de MT h\u00edbrida, las reglas se\nutilizan para ayudar a corregir un motor basado en estad\u00edsticas durante su fase\nde pre y postprocesamiento.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"MT_neuronal\"><\/span>MT neuronal <span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">La\ntraducci\u00f3n autom\u00e1tica neural (NMT) es el \u00faltimo enfoque de la traducci\u00f3n\nautom\u00e1tica que utiliza redes neurales para predecir la probabilidad de un\nconjunto de palabras. Una enorme ventaja de la NMT es que solo requiere una\nfracci\u00f3n de la memoria que necesitan las t\u00e9cnicas de traducci\u00f3n tradicionales\n(enumeradas anteriormente).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aunque\nning\u00fan sistema actual afirma haber encontrado el billete dorado de los sistemas\nde MT, en la pr\u00e1ctica la MT ha demostrado ser extremadamente valiosa para\ncambiar la forma en que se comunican las personas y las empresas de todo el\nmundo. Todo el mundo, desde el ej\u00e9rcito estadounidense hasta Facebook, est\u00e1\ntrabajando duro para construir sus propios sistemas de traducci\u00f3n que acerquen\nal mundo y ayuden a la seguridad nacional. Con el lanzamiento de los\n\u00abpixel buds\u00bb de Google, que afirman proporcionar traducci\u00f3n en tiempo\nreal en innumerables idiomas, \u00bftendr\u00e1n los empresarios o los ni\u00f1os de primaria\ndel futuro poca necesidad de aprender un idioma extranjero?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Como reci\u00e9n\nllegado al aprendizaje autom\u00e1tico, este es el primero de muchos peque\u00f1os posts\ncon el objetivo de proporcionar res\u00famenes \u00fatiles de los subcampos del\naprendizaje autom\u00e1tico, conceptos, algoritmos y flujos de trabajo para pythonistas\ny desarrolladores por igual que est\u00e9n interesados en aprender o trabajar en ML.\nSi encuentras un error o tienes una opini\u00f3n sobre c\u00f3mo he resumido un\ntema\/campo en particular, no tengas miedo de hac\u00e9rmelo saber en los\ncomentarios.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>\u00abTodos necesitamos gente que nos d\u00e9 su opini\u00f3n. As\u00ed es como mejoramos\u00bb. <\/p><cite>Bill Gates<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Hasta la\npr\u00f3xima.<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;6656&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Rate this post&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votos)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;\ufeffLost in Machine Translation: una peque\u00f1a introducci\u00f3n a la 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neuronal.","protected":false},"author":8,"featured_media":6706,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[619],"tags":[279,313,191],"class_list":["post-6656","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-traduccion-automatica","tag-machine-translation","tag-mt","tag-traduccion-ingles"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"robots\" content=\"max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n\t<meta name=\"author\" content=\"Mauri Fernandez\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/www.ibidemgroup.com\/edu\/introduccion-machine-translation-mt\/\" \/>\n\t\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.3\" \/>\n\t\t<meta property=\"og:locale\" content=\"es_ES\" \/>\n\t\t<meta property=\"og:site_name\" content=\"IBIDEM EDU 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