{"id":144979,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/combining-statistical-and-knowledge-based-spoken-language-understanding-in-conditional-models\/"},"modified":"2018-10-16T20:05:30","modified_gmt":"2018-10-17T03:05:30","slug":"combining-statistical-and-knowledge-based-spoken-language-understanding-in-conditional-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/combining-statistical-and-knowledge-based-spoken-language-understanding-in-conditional-models\/","title":{"rendered":"Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive \u2013 it requires joint expertise in natural language processing and speech recognition, and the best practice in language engineering for every new domain. On the other hand, statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications out of large research labs. A generative HMM\/CFG composite model, which integrates easy-to-obtain domain knowledge in a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare the conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the generative HMM\/CFG model, which had already achieved the SLU accuracy at the same level as the best results reported on the ATIS data.<\/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 an utterance. The traditional knowledge-based approach to this problem is very expensive \u2013 it requires joint expertise in natural language processing and speech recognition, and the best practice in language engineering for every new domain. On the other hand, statistical learning approach [&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":"yeyiwang"},{"type":"user_nicename","value":"alexac"},{"type":"user_nicename","value":"milindm"},{"type":"user_nicename","value":"jlee"}],"msr_publishername":"Association for Computational 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Language Understanding","post_name":"spoken-language-understanding","post_type":"msr-project","post_date":"2013-05-01 11:46:32","post_modified":"2019-08-19 14:48:51","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/spoken-language-understanding\/","post_excerpt":"Spoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. 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\/144979","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":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/144979\/revisions"}],"predecessor-version":[{"id":521894,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/144979\/revisions\/521894"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=144979"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=144979"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=144979"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=144979"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=144979"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=144979"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=144979"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=144979"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=144979"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=144979"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=144979"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=144979"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=144979"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}