{"id":"https://openalex.org/W3015159878","doi":"https://doi.org/10.1145/3365679","title":"S <sup>3</sup> -NET","display_name":"S <sup>3</sup> -NET","publication_year":2020,"publication_date":"2020-02-20","ids":{"openalex":"https://openalex.org/W3015159878","doi":"https://doi.org/10.1145/3365679","mag":"3015159878"},"language":"en","primary_location":{"id":"doi:10.1145/3365679","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3365679","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009313002","display_name":"Cheoneum Park","orcid":"https://orcid.org/0000-0001-5386-0483"},"institutions":[{"id":"https://openalex.org/I165507594","display_name":"Kangwon National University","ror":"https://ror.org/01mh5ph17","country_code":"KR","type":"education","lineage":["https://openalex.org/I165507594"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Cheoneum Park","raw_affiliation_strings":["Kangwon National University, Chuncheon, Gangwondo, KOR"],"affiliations":[{"raw_affiliation_string":"Kangwon National University, Chuncheon, Gangwondo, KOR","institution_ids":["https://openalex.org/I165507594"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060901752","display_name":"Hee-Jun Song","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heejun Song","raw_affiliation_strings":["Samsung Research, Seongchon-gil, Seoul, KOR"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Seongchon-gil, Seoul, KOR","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073790847","display_name":"Changki Lee","orcid":"https://orcid.org/0000-0001-8373-1500"},"institutions":[{"id":"https://openalex.org/I165507594","display_name":"Kangwon National University","ror":"https://ror.org/01mh5ph17","country_code":"KR","type":"education","lineage":["https://openalex.org/I165507594"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Changki Lee","raw_affiliation_strings":["Kangwon National University, Chuncheon, Gangwondo, KOR"],"affiliations":[{"raw_affiliation_string":"Kangwon National University, Chuncheon, Gangwondo, KOR","institution_ids":["https://openalex.org/I165507594"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009313002"],"corresponding_institution_ids":["https://openalex.org/I165507594"],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.79867077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"19","issue":"3","first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994999766349792,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7721508741378784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7675918340682983},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.7305589914321899},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5832895636558533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5816642045974731},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5725145936012268},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5495530366897583},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5323190093040466},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48907023668289185},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.463293194770813},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.46208736300468445},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.42457112669944763},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.41145285964012146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1732511818408966},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10780677199363708},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.05968749523162842}],"concepts":[{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7721508741378784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7675918340682983},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.7305589914321899},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5832895636558533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5816642045974731},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5725145936012268},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5495530366897583},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5323190093040466},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48907023668289185},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.463293194770813},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.46208736300468445},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.42457112669944763},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.41145285964012146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1732511818408966},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10780677199363708},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.05968749523162842},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3365679","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3365679","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5699999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1924770834","https://openalex.org/W2126209950","https://openalex.org/W2133564696","https://openalex.org/W2250539671","https://openalex.org/W2507756961","https://openalex.org/W2521709538","https://openalex.org/W2626154462","https://openalex.org/W2740747242","https://openalex.org/W2751185861","https://openalex.org/W2798999921","https://openalex.org/W2801288650","https://openalex.org/W2935047543","https://openalex.org/W2950621961","https://openalex.org/W2951815760","https://openalex.org/W2962739339","https://openalex.org/W2962985038","https://openalex.org/W2963748441"],"related_works":["https://openalex.org/W2912321008","https://openalex.org/W2375873920","https://openalex.org/W2146114872","https://openalex.org/W2324368075","https://openalex.org/W2392060890","https://openalex.org/W1998607122","https://openalex.org/W2972032537","https://openalex.org/W338149487","https://openalex.org/W2112693048","https://openalex.org/W3154107650"],"abstract_inverted_index":{"Machine":[0],"reading":[1],"comprehension":[2],"question":[3],"answering":[4],"(MRC-QA)":[5],"is":[6,26,64,72],"the":[7,11,32,35,79,111,128],"task":[8],"of":[9,13,28,34,114,119],"understanding":[10],"context":[12],"a":[14,19,67,73],"given":[15],"passage":[16,25],"to":[17,41,56,85,96],"find":[18],"correct":[20],"answer":[21],"within":[22],"it.":[23],"A":[24],"composed":[27],"several":[29],"sentences;":[30],"therefore,":[31],"length":[33],"input":[36],"sentence":[37],"becomes":[38],"longer,":[39],"leading":[40],"diminished":[42],"performance.":[43],"In":[44,88],"this":[45,58,135],"article,":[46],"we":[47],"propose":[48],"S":[49,60,90,129],"3":[50,61,91,130],"-NET,":[51,62],"which":[52,63],"adds":[53],"sentence-based":[54],"encoding":[55],"solve":[57],"problem.":[59],"based":[65],"on":[66],"simple":[68],"recurrent":[69,103],"unit":[70],"architecture,":[71],"deep":[74],"learning":[75],"model":[76,132,158],"that":[77,125],"solves":[78],"MRC-QA":[80,109],"by":[81],"applying":[82],"matching":[83],"network":[84,105],"sentence-level":[86],"encoding.":[87],"addition,":[89],"-NET":[92,131],"utilizes":[93],"self-matching":[94],"networks":[95],"compute":[97],"attention":[98],"weight":[99],"for":[100,110,126,155],"its":[101],"own":[102],"neural":[104],"sequences.":[106],"We":[107],"perform":[108],"SQuAD":[112],"dataset":[113,118],"English":[115],"and":[116,139,143,145,150,154,161,165,167,172],"MindsMRC":[117],"Korean.":[120],"The":[121],"experimental":[122],"results":[123],"show":[124],"SQuAD,":[127],"proposed":[133],"in":[134,148,170],"article":[136],"produces":[137],"71.91%":[138],"74.12%":[140],"exact":[141,163],"match":[142,164],"81.02%":[144],"82.34%":[146],"F1":[147,169],"single":[149,171],"ensemble":[151,173],"models,":[152,174],"respectively,":[153],"MindsMRC,":[156],"our":[157],"achieves":[159],"69.43%":[160],"71.28%":[162],"81.53%":[166],"82.77%":[168],"respectively.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}