{"id":"https://openalex.org/W2889736774","doi":"https://doi.org/10.18653/v1/d18-1106","title":"Supervised Domain Enablement Attention for Personalized Domain Classification","display_name":"Supervised Domain Enablement Attention for Personalized Domain Classification","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889736774","doi":"https://doi.org/10.18653/v1/d18-1106","mag":"2889736774"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1106","pdf_url":"https://www.aclweb.org/anthology/D18-1106.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.aclweb.org/anthology/D18-1106.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004011764","display_name":"Joo-Kyung Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joo-Kyung Kim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010305620","display_name":"Young\u2010Bum Kim","orcid":"https://orcid.org/0000-0001-9471-6330"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Young-Bum Kim","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.553,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.492655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":85,"max":86},"biblio":{"volume":null,"issue":null,"first_page":"894","last_page":"899"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9967,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9907,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.82723737},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.71186393},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.55977046}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.82723737},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.782684},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.71186393},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.69090724},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6178572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61708593},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.55977046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49923468},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.48234433},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.47534204},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42281276},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3327676},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.13412401},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09234744},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1106","pdf_url":"https://www.aclweb.org/anthology/D18-1106.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1812.07546","pdf_url":"https://arxiv.org/pdf/1812.07546","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1106","pdf_url":"https://www.aclweb.org/anthology/D18-1106.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.77}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":37,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1689711448","https://openalex.org/W1821462560","https://openalex.org/W1832693441","https://openalex.org/W1902237438","https://openalex.org/W2079735306","https://openalex.org/W2100649405","https://openalex.org/W2133564696","https://openalex.org/W2212703438","https://openalex.org/W2250539671","https://openalex.org/W2253795368","https://openalex.org/W2407610416","https://openalex.org/W2530816535","https://openalex.org/W2572102653","https://openalex.org/W2577255746","https://openalex.org/W2586050494","https://openalex.org/W2767026327","https://openalex.org/W2774983917","https://openalex.org/W2803023299","https://openalex.org/W2949847915","https://openalex.org/W2951970475","https://openalex.org/W2962792802","https://openalex.org/W2962834107","https://openalex.org/W2962910139","https://openalex.org/W2963123301","https://openalex.org/W2963266340","https://openalex.org/W2963454111","https://openalex.org/W2963598809","https://openalex.org/W2963687836","https://openalex.org/W2963768411","https://openalex.org/W2963918774","https://openalex.org/W2964121744","https://openalex.org/W2964201905","https://openalex.org/W2964222566","https://openalex.org/W2964308564","https://openalex.org/W648947103"],"related_works":["https://openalex.org/W4389279194","https://openalex.org/W4376118624","https://openalex.org/W4287591324","https://openalex.org/W4285326772","https://openalex.org/W3170224572","https://openalex.org/W3120400911","https://openalex.org/W3108503355","https://openalex.org/W3107204728","https://openalex.org/W3080832216","https://openalex.org/W2376954173"],"abstract_inverted_index":{"In":[0,38],"large-scale":[1,115],"domain":[2,11,35,124],"classification":[3,36,125],"for":[4,52],"natural":[5],"language":[6],"understanding,":[7],"leveraging":[8],"each":[9],"user's":[10],"enablement":[12,45],"information,":[13],"which":[14,48],"refers":[15],"to":[16,31,81,84,97],"the":[17,23,33,53,58,68,85,89,99,103,110],"preferred":[18],"or":[19],"authenticated":[20],"domains":[21],"by":[22],"user,":[24],"with":[25,63],"attention":[26,46,54,59,76,100],"mechanism":[27],"has":[28],"been":[29],"shown":[30],"improve":[32],"overall":[34],"performance.":[37],"this":[39],"paper,":[40],"we":[41,117],"propose":[42],"a":[43,114],"supervised":[44],"mechanism,":[47],"utilizes":[49],"sigmoid":[50],"activation":[51],"weighting":[55],"so":[56],"that":[57,119],"can":[60],"be":[61,82],"computed":[62],"more":[64],"expressive":[65],"power":[66],"without":[67],"weight":[69],"sum":[70],"constraint":[71],"of":[72,88,102],"softmax":[73],"attention.":[74],"The":[75],"weights":[77],"are":[78],"explicitly":[79],"encouraged":[80],"similar":[83],"corresponding":[86],"elements":[87],"output":[90],"one-hot":[91],"vector,":[92],"and":[93],"self-distillation":[94],"is":[95],"used":[96],"leverage":[98],"information":[101],"other":[104],"enabled":[105],"domains.":[106],"By":[107],"evaluating":[108],"on":[109],"actual":[111],"utterances":[112],"from":[113],"IPDA,":[116],"show":[118],"our":[120],"approach":[121],"significantly":[122],"improves":[123],"performance":[126]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2889736774","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":7}],"updated_date":"2024-12-15T01:31:04.631169","created_date":"2018-09-27"}