{"id":"https://openalex.org/W4311300643","doi":"https://doi.org/10.1145/3565387.3565405","title":"Chinese Machine Reading Comprehension Based on Language Model Containing Knowledge","display_name":"Chinese Machine Reading Comprehension Based on Language Model Containing Knowledge","publication_year":2022,"publication_date":"2022-10-21","ids":{"openalex":"https://openalex.org/W4311300643","doi":"https://doi.org/10.1145/3565387.3565405"},"language":"en","primary_location":{"id":"doi:10.1145/3565387.3565405","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565387.3565405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 6th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"},"type":"conference-paper","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/A5069521178","display_name":"Wentong Chen","orcid":"https://orcid.org/0000-0001-9348-8591"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentong Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-9348-8591","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780601","display_name":"Chunxiao Fan","orcid":"https://orcid.org/0000-0002-3607-4904"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunxiao Fan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-3607-4904","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082763858","display_name":"Yuexin Wu","orcid":"https://orcid.org/0000-0001-9005-5678"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuexin Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-9005-5678","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057593843","display_name":"Yitong Wang","orcid":"https://orcid.org/0000-0002-7265-4455"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yitong Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-7265-4455","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9976999759674072,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9919999837875366,"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/computer-science","display_name":"Computer science","score":0.8280192613601685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6225848197937012},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.577599823474884},{"id":"https://openalex.org/keywords/reading-comprehension","display_name":"Reading comprehension","score":0.5705704092979431},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.560093343257904},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5592839121818542},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.5172055959701538},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4870457947254181},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48076769709587097},{"id":"https://openalex.org/keywords/knowledge-retrieval","display_name":"Knowledge retrieval","score":0.47875967621803284},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.292193204164505},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.13799071311950684},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07015568017959595}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8280192613601685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6225848197937012},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.577599823474884},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.5705704092979431},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.560093343257904},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5592839121818542},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.5172055959701538},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4870457947254181},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48076769709587097},{"id":"https://openalex.org/C2780613888","wikidata":"https://www.wikidata.org/wiki/Q6423394","display_name":"Knowledge retrieval","level":3,"score":0.47875967621803284},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.292193204164505},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.13799071311950684},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07015568017959595},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3565387.3565405","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565387.3565405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 6th International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W102708294","https://openalex.org/W1502957213","https://openalex.org/W1544827683","https://openalex.org/W2022166150","https://openalex.org/W2080133951","https://openalex.org/W2081580037","https://openalex.org/W2127795553","https://openalex.org/W2184957013","https://openalex.org/W2283196293","https://openalex.org/W2288995089","https://openalex.org/W2607303097","https://openalex.org/W2740747242","https://openalex.org/W2741075451","https://openalex.org/W2758430142","https://openalex.org/W2781528640","https://openalex.org/W2791374212","https://openalex.org/W2803457824","https://openalex.org/W2896457183","https://openalex.org/W2899575547","https://openalex.org/W2915480215","https://openalex.org/W2945495287","https://openalex.org/W2950813464","https://openalex.org/W2951561177","https://openalex.org/W2953356739","https://openalex.org/W2970986510","https://openalex.org/W2973840669","https://openalex.org/W2975059944","https://openalex.org/W4391156274","https://openalex.org/W6718053083"],"related_works":["https://openalex.org/W2082438799","https://openalex.org/W1966986837","https://openalex.org/W2360138227","https://openalex.org/W4365808155","https://openalex.org/W1838455177","https://openalex.org/W1489909378","https://openalex.org/W2082296339","https://openalex.org/W2161828220","https://openalex.org/W1972348076","https://openalex.org/W2083863157"],"abstract_inverted_index":{"Machine":[0,71],"reading":[1,64,172],"comprehension":[2,173],"(MRC)":[3],"is":[4,68,82,162],"a":[5,16,53,128,145,152,158,181],"task":[6],"that":[7,37,177],"requires":[8],"machines":[9],"to":[10,62,117,135],"answer":[11],"relevant":[12,44,91],"questions":[13],"based":[14,164],"on":[15,79,109,165,170],"given":[17,60],"context.":[18],"In":[19],"recent":[20],"years,":[21],"it":[22],"has":[23,180],"attracted":[24],"extensive":[25],"attention":[26],"with":[27,185],"the":[28,48,59,76,86,110,119,137,186],"development":[29],"of":[30,55,90,105,121,139],"deep":[31],"learning":[32],"and":[33,65,85,88,150],"big":[34],"data.":[35],"Considering":[36],"human":[38],"beings":[39],"will":[40],"associate":[41],"some":[42],"external":[43,190],"knowledge":[45,57,92,123,130,141],"when":[46],"understanding":[47],"text,":[49],"researchers":[50],"have":[51],"proposed":[52],"method":[54,67,81,179,188],"introducing":[56],"outside":[58],"context":[61],"assist":[63],"this":[66,80,106,166],"called":[69],"Knowledge-Based":[70],"Reading":[72],"Comprehension":[73],"(KBMRC).":[74],"However,":[75],"current":[77],"research":[78],"still":[83,94],"scattered,":[84],"retrieval":[87],"fusion":[89,160],"are":[93],"two":[95],"challenges":[96],"in":[97,100,115,133],"application,":[98],"especially":[99],"Chinese":[101,171],"MRC.":[102],"The":[103,168],"contribution":[104],"paper":[107],"mainly":[108],"following":[111],"three":[112],"points:":[113],"Firstly,":[114],"order":[116,134],"resolve":[118,136],"problem":[120,138],"related":[122,129,140],"retrieval,":[124],"we":[125,143],"build":[126],"up":[127],"set.":[131],"Secondly,":[132],"fusion,":[142],"propose":[144],"negative":[146],"sample":[147],"generation":[148],"strategy":[149],"train":[151],"language":[153],"model":[154,161],"containing":[155],"knowledge.":[156,191],"Finally,":[157],"twin-tower":[159],"constructed":[163],"model.":[167],"experiments":[169],"dataset":[174],"CMRC2018":[175],"show":[176],"our":[178],"certain":[182],"improvement":[183],"compared":[184],"baseline":[187],"without":[189]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
