{"id":168604,"date":"2015-09-01T00:00:00","date_gmt":"2015-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fast-and-easy-language-understanding-for-dialog-systems-with-microsoft-language-understanding-intelligent-service-luis\/"},"modified":"2018-10-16T20:30:44","modified_gmt":"2018-10-17T03:30:44","slug":"fast-and-easy-language-understanding-for-dialog-systems-with-microsoft-language-understanding-intelligent-service-luis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fast-and-easy-language-understanding-for-dialog-systems-with-microsoft-language-understanding-intelligent-service-luis\/","title":{"rendered":"Fast and Easy Language Understanding For Dialog Systems With Microsoft Language Understanding Intelligent Service (LUIS)"},"content":{"rendered":"<div class=\"asset-content\">\n<p>With Language Understanding Intelligent Service (LUIS), developers without machine learning expertise can quickly build and use language understanding models specific to their task. LUIS is entirely cloud-based: developers log into a website, enter a few example utterances and their labels, and then deploy a model to an HTTP endpoint. Utterances sent to the endpoint are logged and can be efficiently labeled using active learning. Visualizations help identify issues, which can be resolved by either adding more labels or by giving hints to the machine learner in the form of features. Altogether, a developer can create and deploy an initial language understanding model in minutes, and easily maintain it as usage of their application grows.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>With Language Understanding Intelligent Service (LUIS), developers without machine learning expertise can quickly build and use language understanding models specific to their task. LUIS is entirely cloud-based: developers log into a website, enter a few example utterances and their labels, and then deploy a model to an HTTP endpoint. Utterances sent to the endpoint are [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-168604","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACL - Association for Computational Linguistics","msr_edition":"Proceedings of 2015 SIGDIAL Conference, 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