{"id":3496,"date":"2020-08-04T21:49:00","date_gmt":"2020-08-04T21:49:00","guid":{"rendered":"https:\/\/staging.novelis.io\/?post_type=scientific-pub&#038;p=3496"},"modified":"2022-02-25T10:50:13","modified_gmt":"2022-02-25T10:50:13","slug":"reseaux-neuronaux-artificiels-pour-les-taches-de-conversion-de-texte-en-langage-sql-etat-de-art","status":"publish","type":"scientific-pub","link":"https:\/\/novelis.io\/fr\/scientific-pub\/reseaux-neuronaux-artificiels-pour-les-taches-de-conversion-de-texte-en-langage-sql-etat-de-art\/","title":{"rendered":"R\u00e9seaux neuronaux artificiels pour les t\u00e2ches de conversion de texte en langage SQL : \u00c9tat de l\u2019art"},"content":{"rendered":"\n<p>D\u00e9couvrez notre article de conf\u00e9rence&nbsp;<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-53187-4_61\" target=\"_blank\" rel=\"noopener\">Artificial Neural Networks for Text-to-SQL task: state of the art<\/a>&nbsp;\u2013&nbsp;<a href=\"https:\/\/link.springer.com\/conference\/smartict\" target=\"_blank\" rel=\"noopener\">Conf\u00e9rence internationale sur les technologies intelligentes de l\u2019information et de la communication \u2013<\/a>&nbsp;<a href=\"https:\/\/link.springer.com\/bookseries\/7818\" target=\"_blank\" rel=\"noopener\">Lecture Notes in Electrical Engineering<\/a>, publi\u00e9 dans Springer Link. Cette \u00e9tude est en anglais.<\/p>\n\n\n\n<p>Merci \u00e0 l\u2019<a href=\"https:\/\/novelis.io\/fr\/laboratoire-rd\/\">\u00e9quipe de recherche de Novelis<\/a> pour son savoir-faire et son expertise.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">&nbsp;<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"resume\">R\u00e9sum\u00e9<\/h2>\n\n\n\n<p>Les bases de donn\u00e9es stockent une quantit\u00e9 importante de donn\u00e9es provenant du monde entier. Toutefois, pour acc\u00e9der \u00e0 ces donn\u00e9es, les utilisateurs doivent comprendre un langage d\u2019interrogation tel que SQL. Afin de faciliter cette t\u00e2che et de rendre l\u2019interaction avec les bases de donn\u00e9es possible pour le monde entier, des recherches sont r\u00e9cemment apparues pour approcher des syst\u00e8mes qui comprennent les questions en langage naturel et les convertissent automatiquement en requ\u00eates SQL. L\u2019objectif de cet article est de fournir l\u2019\u00e9tat de l\u2019art de la t\u00e2che text-to-SQL dans lequel nous pr\u00e9sentons les principaux mod\u00e8les et solutions existants bas\u00e9s sur les r\u00e9seaux de neurones artificiels (ANN), pr\u00e9cis\u00e9ment sur le Deep Learning (DL) et le traitement du langage naturel (NLP). Nous pr\u00e9cisons \u00e9galement les param\u00e8tres exp\u00e9rimentaux de chaque approche, leurs limites ainsi qu\u2019une comparaison des meilleures existantes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">&nbsp;<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a-propos-de-l-etude\">A propos de l\u2019\u00e9tude<\/h2>\n\n\n\n<p>\u201cText-to-SQL task is one of the most important subtask of semantic parsing in natural language processing (NLP). It maps natural language sentences to corresponding SQL queries. In recent years, some state-of-the-art methods with Seq2Seq encoder-decoder architectures (Ilya Sutskever, Oriol Vinyals, Quoc V. Le 2014) [1] are able to obtain more than 80% exact matching accuracy on some complex text-to-SQL benchmarks such as&nbsp;<strong>Atis&nbsp;<\/strong>(Price, 1990; Dahl and al., 1994) [2],&nbsp;<strong>GeoQuery&nbsp;<\/strong>(Zelle and Mooney, 1996) [3],&nbsp;<strong>Restaurants&nbsp;<\/strong>(Tang and Mooney, 2000; Popescu and al., 2003) [4],&nbsp;<strong>Scholar&nbsp;<\/strong>(Iyer and al., 2017) [5],&nbsp;<strong>Academic&nbsp;<\/strong>(Li and Jagadish, 2014) [6],&nbsp;<strong>Yelp&nbsp;<\/strong>(Yaghmazadeh and al., 2017) [7] and&nbsp;<strong>WikiSQL&nbsp;<\/strong>(Zhong and al., 2017) [8].These models seem to have already solved most problems in this area. However, as (Finegan-Dollak et al., 2018) [9] show, because of the problematic task definition in the traditional datasets, most of these mod- els just learn to match semantic parsing results, rather than truly learn to understand the meanings of inputs and generalize to new programs and databases, which led to low precisions on more generic dataset as the case of&nbsp;<strong>Spider (<\/strong>YU, Tao, ZHANG, Rui, YANG, Kai, and al. 2018) [10].\u201d<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"lire-l-article-complet\"><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-53187-4_61\" target=\"_blank\" rel=\"noopener\">Lire l\u2019article complet<\/a><\/h3>\n\n\n\n<p><a href=\"https:\/\/link.springer.com\/bookseries\/7818\" target=\"_blank\" rel=\"noopener\">Lecture Notes in Electrical Engineering<\/a>&nbsp;(LNEE, volume 684)<\/p>\n\n\n\n<p><strong>SpringerLink fournit aux chercheurs l\u2019acc\u00e8s \u00e0 des millions de documents scientifiques provenant de revues, livres, s\u00e9ries, protocoles et ouvrages de r\u00e9f\u00e9rence<\/strong>.<\/p>\n","protected":false},"featured_media":2293,"template":"","categories":[24],"custom_tag":[5,167],"class_list":["post-3496","scientific-pub","type-scientific-pub","status-publish","has-post-thumbnail","hentry","category-publication-scientifique","custom_tag-ai","custom_tag-sql-fr"],"acf":{"summary":"","filter_opacity":"70","subtitle":"","reading_time":"","authors":"","document_to_download":{"upload_a_file":false,"download_without_form":false,"file":false,"url":""},"externel_link":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R\u00e9seaux neuronaux artificiels pour les t\u00e2ches de conversion de texte SQL<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez notre article Artificial Neural Networks for Text-to-SQL task: state of the art\u00a0- Lecture Notes in Electrical 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