{"id":162865,"date":"2012-03-01T00:00:00","date_gmt":"2012-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/towards-deeper-understanding-deep-convex-networks-for-semantic-utterance-classification\/"},"modified":"2018-10-16T21:07:40","modified_gmt":"2018-10-17T04:07:40","slug":"towards-deeper-understanding-deep-convex-networks-for-semantic-utterance-classification","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-deeper-understanding-deep-convex-networks-for-semantic-utterance-classification\/","title":{"rendered":"Towards Deeper Understanding Deep Convex Networks for Semantic Utterance Classification"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Following the recent advances in deep learning techniques, in this paper, we present the application of special type of deep architecture \u2014 deep convex networks (DCNs) \u2014 for semantic utterance classification (SUC). DCNs are shown to have several advantages over deep belief networks (DBNs) including classification accuracy and training scalability. However, adoption of DCNs for SUC comes with non-trivial issues. Specifically, SUC has an extremely sparse input feature space encompassing a very large number of lexical and semantic features. This is about a few thousand times larger than the feature space for acoustic modeling, yet with a much smaller number of training samples. Experimental results we obtained on a domain classification task for spoken language understanding demonstrate the effectiveness of DCNs. The DCN-based method produces higher SUC accuracy than the Boosting-based discriminative classifier with word trigrams.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Following the recent advances in deep learning techniques, in this paper, we present the application of special type of deep architecture \u2014 deep convex networks (DCNs) \u2014 for semantic utterance classification (SUC). DCNs are shown to have several advantages over deep belief networks (DBNs) including classification accuracy and training scalability. However, adoption of DCNs for [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"gokhant","user_id":"31896"},{"type":"user_nicename","value":"deng","user_id":"31602"},{"type":"user_nicename","value":"dilekha","user_id":"31630"},{"type":"user_nicename","value":"xiaohe","user_id":"34880"}],"msr_publishername":"IEEE International Confrence on Acoustics, Speech, and Signal Processing 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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. 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