{"id":166863,"date":"2014-09-01T00:00:00","date_gmt":"2014-09-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/detecting-out-of-domain-utterances-addressed-to-a-virtual-personal-assistant\/"},"modified":"2018-10-16T21:06:28","modified_gmt":"2018-10-17T04:06:28","slug":"detecting-out-of-domain-utterances-addressed-to-a-virtual-personal-assistant","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/detecting-out-of-domain-utterances-addressed-to-a-virtual-personal-assistant\/","title":{"rendered":"Detecting Out-Of-Domain Utterances Addressed to a Virtual Personal Assistant"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Conversational understanding systems, especially virtual personal assistants (VPAs), perform \u201ctargeted\u201d natural language understanding, assuming their users stay within the walled gardens of covered domains, and back-off to generic web search otherwise. However, users usually do not know the concept of domains and sometimes simply do not distinguish the system from simple voice search. Hence it becomes an important problem to identify these rejected out-of-domain utterances which are actually intended for the VPA. This paper presents a study tackling this new task, showing that how one utters a request is more important for this task than what is uttered, resembling addressee detection or dialog act tagging. To this end, syntactic and semantic parse \u201cstructure\u201d features are extracted in addition to lexical features to train a binary SVM classifier using a large number of random web search queries and VPA utterances from multiple domains. We present controlled experiments leaving one domain out and check the precision of the model when combined with unseen queries. Our results indicate that such structured features result in higher precision especially when the test domain bears little resemblance to the existing domains.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Conversational understanding systems, especially virtual personal assistants (VPAs), perform \u201ctargeted\u201d natural language understanding, assuming their users stay within the walled gardens of covered domains, and back-off to generic web search otherwise. However, users usually do not know the concept of domains and sometimes simply do not distinguish the system from simple voice search. Hence it [&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":"anoopd"},{"type":"user_nicename","value":"dilekha"}],"msr_publishername":"ISCA - International Speech Communication Association","msr_publisher_other":"","msr_booktitle":"Proceedings of Interspeech","msr_chapter":"","msr_edition":"Proceedings of Interspeech","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":"","msr_conference_name":"Proceedings of Interspeech","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":"2014-09-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2014,"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":[13545],"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-166863","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"ISCA - 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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:\/\/cm-edgetun.pages.dev\/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:\/\/cm-edgetun.pages.dev\/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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