{"id":154831,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-semantically-structured-language-model\/"},"modified":"2018-10-16T21:39:39","modified_gmt":"2018-10-17T04:39:39","slug":"a-semantically-structured-language-model","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-semantically-structured-language-model\/","title":{"rendered":"A Semantically Structured Language Model"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper we propose a semantically structured language (SSLM) model that significantly reduces the authoring load required over the traditional manually derived grammar when developing a spoken language system. At the same time, the SSLM results in an understanding error rate which is roughly half as large as that of the manually authored grammar. The proposed model combines the advantages of both statistical word n-grams and context-free grammars. When the SSLM directly acts as the recognizer&#8217;s language model there&#8217;s a significant reduction in understanding error rate over the case where it is applied only at the output of a recognizer driven by an word n-gram language model.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we propose a semantically structured language (SSLM) model that significantly reduces the authoring load required over the traditional manually derived grammar when developing a spoken language system. At the same time, the SSLM results in an understanding error rate which is roughly half as large as that of the manually authored grammar. 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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. 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