{"id":167941,"date":"2013-12-12T00:00:00","date_gmt":"2013-12-12T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/convolutional-neural-network-based-triangular-crf-for-joint-intent-detection-and-slot-filling\/"},"modified":"2018-10-16T20:02:28","modified_gmt":"2018-10-17T03:02:28","slug":"convolutional-neural-network-based-triangular-crf-for-joint-intent-detection-and-slot-filling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/convolutional-neural-network-based-triangular-crf-for-joint-intent-detection-and-slot-filling\/","title":{"rendered":"Convolutional Neural Network Based Triangular CRF for Joint Intent Detection and Slot Filling"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe a joint model for intent detection and slot filling based on convolutional neural networks (CNN). The proposed architecture can be perceived as a neural network (NN) version of the <em>triangular<\/em> CRF model (TriCRF), in which the intent label and the slot sequence are modeled jointly and their dependencies are exploited. Our slot filling component is a <em>globally<\/em> normalized CRF style model, as opposed to left-toright models in recent NN based slot taggers. Its features are automatically extracted through CNN layers and shared by the intent model. We show that our slot model component generates state-of-the-art results, outperforming CRF significantly. Our joint model outperforms the standard TriCRF by 1% absolute for both intent and slot. On a number of other domains, our joint model achieves 0.7 &#8211; 1%, and 0.9 &#8211; 2.1% absolute gains over the independent modeling approach for intent and slot respectively.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe a joint model for intent detection and slot filling based on convolutional neural networks (CNN). The proposed architecture can be perceived as a neural network (NN) version of the triangular CRF model (TriCRF), in which the intent label and the slot sequence are modeled jointly and their dependencies are exploited. Our slot filling [&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":"","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":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2013-12-12","msr_highlight_text":"","msr_notes":"Best Paper Award","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2013,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13554],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-167941","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"IEEE - 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