{"id":"https://openalex.org/W3171666028","doi":"https://doi.org/10.1109/cscwd49262.2021.9437862","title":"Incorporating Domain Knowledge and Semantic Information into Language Models for Commonsense Question Answering","display_name":"Incorporating Domain Knowledge and Semantic Information into Language Models for Commonsense Question Answering","publication_year":2021,"publication_date":"2021-05-05","ids":{"openalex":"https://openalex.org/W3171666028","doi":"https://doi.org/10.1109/cscwd49262.2021.9437862","mag":"3171666028"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd49262.2021.9437862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd49262.2021.9437862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-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":null,"display_name":"Ruiying Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092257","display_name":"China Guangzhou Analysis and Testing Center","ror":"https://ror.org/00d6bjs95","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210092257"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruiying Zhou","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China","institution_ids":["https://openalex.org/I4210092257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081024505","display_name":"Keke Tian","orcid":"https://orcid.org/0000-0003-2281-5641"},"institutions":[{"id":"https://openalex.org/I4210092257","display_name":"China Guangzhou Analysis and Testing Center","ror":"https://ror.org/00d6bjs95","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210092257"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keke Tian","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China","institution_ids":["https://openalex.org/I4210092257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076868018","display_name":"Hanjiang Lai","orcid":"https://orcid.org/0000-0001-8057-6744"},"institutions":[{"id":"https://openalex.org/I4210092257","display_name":"China Guangzhou Analysis and Testing Center","ror":"https://ror.org/00d6bjs95","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210092257"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanjiang Lai","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, P.R.China","institution_ids":["https://openalex.org/I4210092257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017205177","display_name":"Jian Yin","orcid":"https://orcid.org/0000-0002-1214-5384"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yin","raw_affiliation_strings":["School of Artificial Intelligence, Sun Yat-sen University"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Sun Yat-sen University","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210092257"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54164627,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1160","last_page":"1165"},"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":0.9976000189781189,"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":0.9948999881744385,"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/commonsense-knowledge","display_name":"Commonsense knowledge","score":0.859123706817627},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.8389135599136353},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8266189098358154},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5601551532745361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5460992455482483},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5324987173080444},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5041052103042603},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48680922389030457},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4661855399608612},{"id":"https://openalex.org/keywords/open-domain","display_name":"Open domain","score":0.4518354833126068},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3341848850250244}],"concepts":[{"id":"https://openalex.org/C30542707","wikidata":"https://www.wikidata.org/wiki/Q1603203","display_name":"Commonsense knowledge","level":3,"score":0.859123706817627},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8389135599136353},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8266189098358154},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5601551532745361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5460992455482483},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5324987173080444},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5041052103042603},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48680922389030457},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4661855399608612},{"id":"https://openalex.org/C2993776861","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Open domain","level":3,"score":0.4518354833126068},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3341848850250244},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd49262.2021.9437862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd49262.2021.9437862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8597717727","display_name":null,"funder_award_id":"U1611264,U1811261,61602530","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1599016936","https://openalex.org/W1950324769","https://openalex.org/W2050482109","https://openalex.org/W2061397531","https://openalex.org/W2073302931","https://openalex.org/W2080858550","https://openalex.org/W2145755360","https://openalex.org/W2267020232","https://openalex.org/W2275056699","https://openalex.org/W2466175319","https://openalex.org/W2519887557","https://openalex.org/W2561529111","https://openalex.org/W2626778328","https://openalex.org/W2740432174","https://openalex.org/W2806055002","https://openalex.org/W2886983157","https://openalex.org/W2890894339","https://openalex.org/W2896457183","https://openalex.org/W2898695519","https://openalex.org/W2946609015","https://openalex.org/W2962739339","https://openalex.org/W2962781380","https://openalex.org/W2962833140","https://openalex.org/W2963101081","https://openalex.org/W2963159690","https://openalex.org/W2963341956","https://openalex.org/W2963583512","https://openalex.org/W2963995027","https://openalex.org/W2964015378","https://openalex.org/W2964207259","https://openalex.org/W2965373594","https://openalex.org/W2970062726","https://openalex.org/W2970597249","https://openalex.org/W2970780738","https://openalex.org/W2974353418","https://openalex.org/W2983995706","https://openalex.org/W2998374885","https://openalex.org/W4211156271","https://openalex.org/W4385245566","https://openalex.org/W6635469476","https://openalex.org/W6681290437","https://openalex.org/W6730529904","https://openalex.org/W6755829550","https://openalex.org/W6768116812","https://openalex.org/W6922068600"],"related_works":["https://openalex.org/W2391533720","https://openalex.org/W2951097643","https://openalex.org/W4309395021","https://openalex.org/W3091989500","https://openalex.org/W3215363805","https://openalex.org/W204133468","https://openalex.org/W2991310128","https://openalex.org/W4307481286","https://openalex.org/W2395174199","https://openalex.org/W4226441484"],"abstract_inverted_index":{"Commonsense":[0],"question":[1],"answering":[2],"(CSQA)":[3],"aims":[4],"to":[5,12,55,68,80,110,132,143,147],"answer":[6,113],"questions":[7],"which":[8],"require":[9],"the":[10,23,49,56,95,102,134,145,164,167],"system":[11,146],"understand":[13],"related":[14,96],"commonsense":[15,97],"knowledge":[16,60,83,103],"that":[17,34],"is":[18],"not":[19],"explicitly":[20],"expressed":[21],"in":[22,28,62],"given":[24],"context.":[25],"Recent":[26],"advance":[27],"neural":[29],"language":[30,88],"models":[31,66],"(e.g.,":[32,61],"BERT)":[33],"are":[35,125],"pre-trained":[36],"on":[37,44,51,159],"a":[38,149],"large-scale":[39],"text":[40],"corpus":[41],"and":[42,84,112,127],"fine-tuned":[43],"downstream":[45],"tasks":[46,124],"has":[47],"boosted":[48],"performance":[50],"CSQA.":[52],"However,":[53],"due":[54],"lack":[57],"of":[58,152,166],"domain":[59,82,135],"social":[63],"situations),":[64],"these":[65],"fail":[67],"reason":[69],"about":[70],"specific":[71],"tasks.":[72],"In":[73],"this":[74],"work,":[75],"we":[76,100,138],"propose":[77],"an":[78],"approach":[79],"incorporate":[81],"semantic":[85,117],"information":[86],"into":[87],"model":[89],"based":[90,120],"approaches":[91],"for":[92],"better":[93,150],"understanding":[94,151],"knowledge.":[98,136],"Firstly,":[99],"extract":[101],"from":[104],"existing":[105],"resources":[106],"by":[107],"jointly":[108],"learning":[109],"ask":[111],"as":[114,116],"well":[115],"role":[118],"labeling":[119],"answering.":[121],"These":[122],"two":[123],"correlated":[126],"can":[128],"reinforce":[129],"each":[130],"other":[131],"discover":[133],"Then,":[137],"utilize":[139],"Semantic":[140],"Role":[141],"Labeling":[142],"enable":[144],"gain":[148],"relations":[153],"among":[154],"relevant":[155],"entities.":[156],"Experimental":[157],"results":[158],"several":[160],"CSQA":[161],"benchmarks":[162],"demonstrate":[163],"effectiveness":[165],"proposed":[168],"approach.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
