{"id":163449,"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\/translating-natural-language-utterances-to-search-queries-for-slu-domain-detection-using-query-click-logs\/"},"modified":"2018-10-16T22:02:28","modified_gmt":"2018-10-17T05:02:28","slug":"translating-natural-language-utterances-to-search-queries-for-slu-domain-detection-using-query-click-logs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/translating-natural-language-utterances-to-search-queries-for-slu-domain-detection-using-query-click-logs\/","title":{"rendered":"Translating Natural Language Utterances to Search Queries for SLU Domain Detection Using Query Click Logs"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Logs of user queries from a search engine (such as Bing or Google) together with the links clicked provide valuable implicit feedback to improve statistical spoken language  nderstanding (SLU) models. However, the form of natural language utterances occurring in spoken interactions with a computer differs stylistically from that of keyword search  queries. In this paper, we propose a machine translation approach to learn a mapping from  natural language utterances to search queries. We train statistical translation models,  using task and domain independent semantically equivalent natural language and keyword search query pairs mined from the search query click logs. We then extend our previous  work on enriching the existing classification feature sets for input utterance domain  detection with features computed using the click distribution over a set of clicked URLs  from search engine query click logs of user utterances with automatically translated queries. This approach results in significant improvements for domain detection, especially when detecting the domains of user utterances that are formulated as natural language queries and effectively complements to the earlier work using syntactic transformations.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Logs of user queries from a search engine (such as Bing or Google) together with the links clicked provide valuable implicit feedback to improve statistical spoken language nderstanding (SLU) models. However, the form of natural language utterances occurring in spoken interactions with a computer differs stylistically from that of keyword search queries. In this paper, [&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"},{"type":"user_nicename","value":"lheck"},{"type":"user_nicename","value":"dilekha"}],"msr_publishername":"IEEE International Conference on Acoustics, Speech, and Signal Processing 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At the same time, a recent surge of activity and progress on semantic web-related concepts from the large search-engine companies represents a potential alternative to the manually intensive design of spoken language processing systems. Standards such as schema.org have been established for schemas&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171393"}]}},{"ID":171150,"post_title":"Spoken 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. 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