{"id":154848,"date":"2005-01-01T00:00:00","date_gmt":"2005-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/statistical-spoken-language-understanding-from-generative-model-to-conditional-model\/"},"modified":"2018-10-16T21:42:40","modified_gmt":"2018-10-17T04:42:40","slug":"statistical-spoken-language-understanding-from-generative-model-to-conditional-model","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/statistical-spoken-language-understanding-from-generative-model-to-conditional-model\/","title":{"rendered":"Statistical Spoken Language Understanding: from Generative Model to Conditional Model"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in a user\u2019s utterance. Traditionally the problem is solved with a knowledge-based approach. In the past decade many data-driven statistical models have been proposed, all of them are in the generative framework. In our previous study, we have introduced a HMM\/CFG composite model. It is a generative model that integrates knowledge-based approach in a statistical learning framework. We have investigated similar integration of prior knowledge and statistical learning in the framework of conditional models recently. This extended summary describes our experiences and presents some preliminary results, which shows a 17% slot error rate reduction over the generative model.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in a user\u2019s utterance. Traditionally the problem is solved with a knowledge-based approach. In the past decade many data-driven statistical models have been proposed, all of them are in the generative framework. In our previous study, we have introduced a HMM\/CFG composite model. 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The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods. Projects Deeper Understanding: Moving\u00a0beyond shallow targeted understanding towards building domain independent SLU models. Scaling SLU: Quickly bootstrapping SLU&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171150"}]}},{"ID":170147,"post_title":"Understand User's Intent from Speech and Text","post_name":"understand-users-intent-from-speech-and-text","post_type":"msr-project","post_date":"2008-12-17 11:20:26","post_modified":"2019-08-19 15:33:37","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/understand-users-intent-from-speech-and-text\/","post_excerpt":"Understanding what users like to do\/need to get is critical in human computer interaction. When natural user interface like speech or natural language is used in human-computer interaction, such as in a spoken dialogue system or with an internet search engine, language understanding becomes an important issue. Intent understanding is about identifying the action a user wants a computer to take or the information she\/he would like to obtain, conveyed in a spoken utterance or&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/170147"}]}},{"ID":169461,"post_title":"Automatic Grammar Induction","post_name":"automatic-grammar-induction","post_type":"msr-project","post_date":"2002-02-19 14:32:24","post_modified":"2019-08-14 14:41:22","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/automatic-grammar-induction\/","post_excerpt":"Automatic learning of speech recognition grammars from example sentences to ease the development of spoken language systems. Researcher Ye-Yi Wang wants to have more time for vacation, so he is teaching his computer to do some work for him. Wang has been working on Spoken Language Understanding for the MiPad project since he was hired to Microsoft Research. He has developed a robust parser and the understanding grammars for several projects. \"Grammar development is painful&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169461"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/154848","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/154848\/revisions"}],"predecessor-version":[{"id":538341,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/154848\/revisions\/538341"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=154848"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=154848"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=154848"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=154848"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=154848"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=154848"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=154848"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=154848"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=154848"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=154848"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=154848"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=154848"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=154848"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}