{"id":238145,"date":"2013-12-01T00:00:00","date_gmt":"2013-12-01T08:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/unsupervised-induction-and-filling-of-semantic-slots-for-spoken-dialogue-systems-using-frame-semantic-parsing\/"},"modified":"2018-10-16T19:59:41","modified_gmt":"2018-10-17T02:59:41","slug":"unsupervised-induction-and-filling-of-semantic-slots-for-spoken-dialogue-systems-using-frame-semantic-parsing","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/unsupervised-induction-and-filling-of-semantic-slots-for-spoken-dialogue-systems-using-frame-semantic-parsing\/","title":{"rendered":"Unsupervised Induction and Filling of Semantic Slots for Spoken Dialogue Systems Using Frame-Semantic Parsing"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Spoken dialogue systems typically use predefined semantic slots to parse users\u2019 natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 74.13% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Spoken dialogue systems typically use predefined semantic slots to parse users\u2019 natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the [&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":null,"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU 2013)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting\/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.","msr_conference_name":"Proceedings of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU 2013)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Yun-Nung Chen, William Yang Wang, Alexander I. 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