{"id":"https://openalex.org/W3032717904","doi":"https://doi.org/10.18653/v1/2020.acl-main.20","title":"Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs","display_name":"Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3032717904","doi":"https://doi.org/10.18653/v1/2020.acl-main.20","mag":"3032717904"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2020.acl-main.20","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.acl-main.20","pdf_url":"https://www.aclweb.org/anthology/2020.acl-main.20.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/2020.acl-main.20.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021139954","display_name":"Dong Bok Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong Bok Lee","raw_affiliation_strings":["KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070831085","display_name":"Seanie Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seanie Lee","raw_affiliation_strings":["KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055553375","display_name":"Woo Tae Jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Woo Tae Jeong","raw_affiliation_strings":["KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370751","display_name":"Donghwan Kim","orcid":"https://orcid.org/0000-0003-1411-8635"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donghwan Kim","raw_affiliation_strings":["KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070302452","display_name":"Sung Ju Hwang","orcid":"https://orcid.org/0000-0002-9675-2324"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sung Ju Hwang","raw_affiliation_strings":["KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST 1 , AITRICS 2 , 42Maru Inc. 3 , South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":2.4224,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91306966,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"208","last_page":"224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9995999932289124,"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":0.9961000084877014,"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/computer-science","display_name":"Computer science","score":0.759341299533844},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7196316719055176},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6787487864494324},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6422030925750732},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5987938642501831},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5816987752914429},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.567283570766449},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.517572283744812},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47565338015556335},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4328903555870056},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4291529655456543},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4100446105003357},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3965594172477722},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35835349559783936},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.20978915691375732}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759341299533844},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7196316719055176},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6787487864494324},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6422030925750732},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5987938642501831},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5816987752914429},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.567283570766449},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.517572283744812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47565338015556335},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4328903555870056},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4291529655456543},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4100446105003357},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3965594172477722},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35835349559783936},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.20978915691375732},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/2020.acl-main.20","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.acl-main.20","pdf_url":"https://www.aclweb.org/anthology/2020.acl-main.20.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2005.13837","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.13837","pdf_url":"https://arxiv.org/pdf/2005.13837","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3032717904","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2005.13837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2005.13837","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2005.13837","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/v1/2020.acl-main.20","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2020.acl-main.20","pdf_url":"https://www.aclweb.org/anthology/2020.acl-main.20.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7699999809265137,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1343409141","display_name":null,"funder_award_id":"2016-0-00563","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G1769687045","display_name":null,"funder_award_id":"2019-0-00075","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G2148870006","display_name":null,"funder_award_id":"2019-0-00075","funder_id":"https://openalex.org/F4320324161","funder_display_name":"Korea Advanced Institute of Science and Technology"},{"id":"https://openalex.org/G3338737308","display_name":null,"funder_award_id":"NRF-2018R1A5A1059921","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5415505637","display_name":null,"funder_award_id":"2019-0-00075","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G7064264939","display_name":null,"funder_award_id":"2019-0-00075","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8190487336","display_name":null,"funder_award_id":"2018R1A5A1059921","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320324161","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3032717904.pdf","grobid_xml":"https://content.openalex.org/works/W3032717904.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W803028973","https://openalex.org/W1531374185","https://openalex.org/W1902237438","https://openalex.org/W2099471712","https://openalex.org/W2101105183","https://openalex.org/W2123301721","https://openalex.org/W2160204597","https://openalex.org/W2250425483","https://openalex.org/W2294059674","https://openalex.org/W2394571815","https://openalex.org/W2547875792","https://openalex.org/W2548228487","https://openalex.org/W2606333299","https://openalex.org/W2610891036","https://openalex.org/W2624022918","https://openalex.org/W2740747242","https://openalex.org/W2753613501","https://openalex.org/W2753738274","https://openalex.org/W2803595284","https://openalex.org/W2803832867","https://openalex.org/W2804292122","https://openalex.org/W2804340650","https://openalex.org/W2807750936","https://openalex.org/W2859444450","https://openalex.org/W2886505372","https://openalex.org/W2888812214","https://openalex.org/W2889670144","https://openalex.org/W2890166583","https://openalex.org/W2890940245","https://openalex.org/W2891946694","https://openalex.org/W2911857455","https://openalex.org/W2912924812","https://openalex.org/W2917436183","https://openalex.org/W2949782788","https://openalex.org/W2949849869","https://openalex.org/W2949961827","https://openalex.org/W2951873722","https://openalex.org/W2962717047","https://openalex.org/W2962717182","https://openalex.org/W2962739339","https://openalex.org/W2962977247","https://openalex.org/W2963175042","https://openalex.org/W2963223306","https://openalex.org/W2963241825","https://openalex.org/W2963323070","https://openalex.org/W2963339397","https://openalex.org/W2963341956","https://openalex.org/W2963714898","https://openalex.org/W2963748441","https://openalex.org/W2963790827","https://openalex.org/W2963879591","https://openalex.org/W2963938442","https://openalex.org/W2964121744","https://openalex.org/W2971274815","https://openalex.org/W2971898464","https://openalex.org/W2988421999","https://openalex.org/W2988673764"],"related_works":["https://openalex.org/W3035500185","https://openalex.org/W2963748441","https://openalex.org/W2963341956","https://openalex.org/W2962717047","https://openalex.org/W2963403868","https://openalex.org/W2962977247","https://openalex.org/W2949849869","https://openalex.org/W2912924812","https://openalex.org/W3082274269","https://openalex.org/W2890166583","https://openalex.org/W2804292122","https://openalex.org/W2606333299","https://openalex.org/W2101105183","https://openalex.org/W3102402592","https://openalex.org/W3019157676","https://openalex.org/W2988421999","https://openalex.org/W2963969878","https://openalex.org/W2963175042","https://openalex.org/W2951873305","https://openalex.org/W1531374185"],"abstract_inverted_index":{"One":[0],"of":[1,13,56,112,162],"the":[2,11,38,49,83,110,113,119,129],"most":[3],"crucial":[4],"challenges":[5],"in":[6],"question":[7],"answering":[8],"(QA)":[9,23],"is":[10,18,40],"scarcity":[12],"labeled":[14],"data,":[15],"since":[16],"it":[17],"costly":[19],"to":[20,36,41,90],"obtain":[21],"question-answer":[22],"pairs":[24,46,75,89,122,133],"for":[25,72,136,164],"a":[26,66,160],"target":[27],"text":[28],"domain":[29],"with":[30],"human":[31],"annotation.":[32],"An":[33],"alternative":[34],"approach":[35],"tackle":[37],"problem":[39,50],"use":[42],"automatically":[43],"generated":[44,87,120,130],"QA":[45,74,88,114,121],"from":[47,53],"either":[48],"context":[51],"or":[52,125],"large":[54],"amount":[55],"unstructured":[57,77],"texts":[58,78],"(e.g.":[59],"Wikipedia).":[60],"In":[61],"this":[62],"work,":[63],"we":[64],"propose":[65],"hierarchical":[67],"conditional":[68],"variational":[69],"autoencoder":[70],"(HCVAE)":[71],"generating":[73],"given":[76],"as":[79],"contexts,":[80],"while":[81],"maximizing":[82],"mutual":[84],"information":[85],"between":[86],"ensure":[91],"their":[92],"consistency.":[93],"We":[94],"validate":[95],"our":[96,146],"Information":[97],"Maximizing":[98],"Hierarchical":[99],"Conditional":[100],"Variational":[101],"AutoEncoder":[102],"(Info-HCVAE)":[103],"on":[104,155],"several":[105],"benchmark":[106],"datasets":[107],"by":[108,126],"evaluating":[109],"performance":[111,150],"model":[115,147],"(BERT-base)":[116],"using":[117,127,158],"only":[118,159],"(QA-based":[123],"evaluation)":[124],"both":[128,156],"and":[131],"human-labeled":[132],"(semisupervised":[134],"learning)":[135],"training,":[137],"against":[138],"stateof-the-art":[139],"baseline":[140],"models.":[141],"The":[142],"results":[143],"show":[144],"that":[145],"obtains":[148],"impressive":[149],"gains":[151],"over":[152],"all":[153],"baselines":[154],"tasks,":[157],"fraction":[161],"data":[163],"training.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":3}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}