Le Bilingual Evaluation Understudy (BLEU) est une m\u00e9thode automatique pour \u00e9valuer la qualit\u00e9 des traductions g\u00e9n\u00e9r\u00e9es par une machine. Qu\u2019est-ce que Bilingual Evaluation Understudy\u00a0? C\u2019est un algorithme qui compare une traduction automatique \u00e0 une ou plusieurs traductions de r\u00e9f\u00e9rence humaines et calcule un score refl\u00e9tant la similarit\u00e9 entre elles.<\/p>\n
BLEU fonctionne en comparant des s\u00e9quences de mots (n-grams) entre la traduction candidate de la machine et les traductions de r\u00e9f\u00e9rence. Plus il y a de n-grams en commun, plus le score BLEU est \u00e9lev\u00e9, indiquant une meilleure qualit\u00e9 de traduction. Imaginez que vous demandiez \u00e0 plusieurs personnes de traduire une phrase de l’anglais vers le fran\u00e7ais. BLEU agit comme un juge impartial qui compare la traduction d’une machine \u00e0 celles des humains, en v\u00e9rifiant la concordance des groupes de mots.<\/p>\n
BLEU est un outil essentiel en traitement automatique du langage car il permet d’\u00e9valuer rapidement et automatiquement de grands volumes de traductions. Cela est crucial pour l’entra\u00eenement et l’am\u00e9lioration des mod\u00e8les de traduction automatique. Il offre une mesure objective de la qualit\u00e9, permettant de comparer diff\u00e9rentes approches et de suivre les progr\u00e8s au fil du temps. Par exemple, un d\u00e9veloppeur peut utiliser BLEU pour mesurer l’am\u00e9lioration de son mod\u00e8le apr\u00e8s ajustement des param\u00e8tres.<\/p>\n
Le Bilingual Evaluation Understudy (BLEU) est une m\u00e9thode automatique pour \u00e9valuer la qualit\u00e9 des traductions g\u00e9n\u00e9r\u00e9es par une machine. Qu\u2019est-ce que Bilingual Evaluation Understudy\u00a0? C\u2019est un algorithme qui compare une traduction automatique \u00e0 une ou plusieurs traductions de r\u00e9f\u00e9rence humaines et calcule un score refl\u00e9tant la similarit\u00e9 entre elles. Comment fonctionne Bilingual Evaluation Understudy\u00a0? BLEU […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[120],"tags":[599,597,598,319],"class_list":["post-1153","post","type-post","status-publish","format-standard","hentry","category-b","tag-bilingual-evaluation-understudy","tag-metrique-devaluation","tag-n-grams","tag-traduction-automatique"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"","author_link":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Le Bilingual Evaluation Understudy (BLEU) est une m\u00e9thode automatique pour \u00e9valuer la qualit\u00e9 des traductions g\u00e9n\u00e9r\u00e9es par une machine. Qu\u2019est-ce que Bilingual Evaluation Understudy\u00a0? C\u2019est un algorithme qui compare une traduction automatique \u00e0 une ou plusieurs traductions de r\u00e9f\u00e9rence humaines et calcule un score refl\u00e9tant la similarit\u00e9 entre elles. Comment fonctionne Bilingual Evaluation Understudy\u00a0? BLEU\u2026","_links":{"self":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts\/1153","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/comments?post=1153"}],"version-history":[{"count":0,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/posts\/1153\/revisions"}],"wp:attachment":[{"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/media?parent=1153"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/categories?post=1153"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/happynumeric.com\/lexique-intelligence-artificielle\/wp-json\/wp\/v2\/tags?post=1153"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}