{"id":593,"date":"2020-06-03T22:28:57","date_gmt":"2020-06-03T16:58:57","guid":{"rendered":"https:\/\/aimonks.com\/aibytes\/?p=593"},"modified":"2020-06-03T22:28:59","modified_gmt":"2020-06-03T16:58:59","slug":"xtreme-a-benchmark-to-spur-development-of-multilingual-ai-models","status":"publish","type":"post","link":"https:\/\/aimonks.com\/aibytes\/2020\/06\/03\/xtreme-a-benchmark-to-spur-development-of-multilingual-ai-models\/","title":{"rendered":"Xtreme- A Benchmark to Spur Development of Multilingual AI Models"},"content":{"rendered":"<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">&lt; 1<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<p><strong>Natural language processing<\/strong>, a subdivision of <strong>artificial intelligence<\/strong>, &nbsp;<strong>linguistics<\/strong>,&nbsp;<strong>information engineering<\/strong>, and&nbsp;<strong>computer science<\/strong>. It is concerned with the interactions between human languages and computers, in particular how to program computers to analyze and process large amounts of&nbsp;natural language&nbsp;data. In research, <strong>Google <\/strong>found that existing models like multilingual <strong>BERT<\/strong>, <strong>XML<\/strong>, <strong>XML-R<\/strong>, and <strong>M4 <\/strong>achieve close to human performance on most existing tasks in English; performance is significantly lower in many other <strong>languages<\/strong>.<br> The release of Google&#8217;s <strong>NLP <\/strong>systems benchmark <strong>Xtreme<\/strong>, consisting of <strong>nine tasks<\/strong>, aims to bridge this gap. Researchers believe it can evaluate the AI model&#8217;s capabilities to capture <strong>knowledge <\/strong>shared across languages so it can be useful for a growing number of natural language applications. The aim is to allow leverage of <strong>data<\/strong>&#8211;<strong>sparse languages <\/strong>to train robust <strong>machine learning <\/strong>models.<br> How does Xtreme work?<br> \u2981    Diverse languages having coverage of existing tasks and the availability of training data are selected.<br> \u2981    The models must be pre-trained on multilingual texts to encourage cross-lingual learning.<br> \u2981    Xtreme evaluates these models with languages that may\/may not have task data available.<br> \u2981    Then it compares the two and gives a combined score by obtaining zero-shot scores on all tasks.<br> Since a massive gap between English and other languages remain quite evident, Xtreme could be a step towards catalyzing research in <strong>multilingual transfer learning<\/strong>.<\/p>\n\n\n\n<p> #AIMonks #AI #ArtificialIntelligence #NLP #XML #BERT #XML-4 #M4 #Google #Xtreme #Multilingual #language #Knowledge<\/p>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">&lt; 1<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> Natural language processing, a subdivision of artificial intelligence, &nbsp;linguistics,&nbsp;information engineering, and&nbsp;computer science. It is concerned with the interactions between human languages and computers, in particular how to program computers to analyze and process large amounts of&nbsp;natural language&nbsp;data. In research, Google found that existing models like multilingual BERT, XML, XML-R, and [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":594,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[61],"tags":[35,24,25,566,93,288,85,568,570,88,567,569],"class_list":["post-593","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-transformation","tag-ai","tag-ai-monks","tag-artificial-intelligence","tag-bert","tag-google","tag-knowledge","tag-language","tag-m4","tag-multilingual","tag-nlp","tag-xml-4","tag-xtreme"],"rttpg_featured_image_url":{"full":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"landscape":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"portraits":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"thumbnail":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual-150x150.jpg",150,150,true],"medium":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"large":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"1536x1536":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"2048x2048":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false],"hestia-blog":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/06\/multilingual.jpg",270,187,false]},"rttpg_author":{"display_name":"AI Bytes","author_link":"https:\/\/aimonks.com\/aibytes\/author\/aibytes_kashika\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/aimonks.com\/aibytes\/category\/daily-bytes\/digital-transformation\/\" rel=\"category tag\">Digital Transformation<\/a>","rttpg_excerpt":"Reading Time: &lt; 1 minutes Natural language processing, a subdivision of artificial intelligence, &nbsp;linguistics,&nbsp;information engineering, and&nbsp;computer science. It is concerned with the interactions between human languages and computers, in particular how to program computers to analyze and process large amounts of&nbsp;natural language&nbsp;data. In research, Google found that existing models like multilingual BERT, XML, XML-R, and&hellip;","_links":{"self":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/593","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/comments?post=593"}],"version-history":[{"count":1,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/593\/revisions"}],"predecessor-version":[{"id":595,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/593\/revisions\/595"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/media\/594"}],"wp:attachment":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/media?parent=593"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/categories?post=593"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/tags?post=593"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}