{"id":"https://openalex.org/W3003604522","doi":"https://doi.org/10.1145/3366423.3380270","title":"Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus","display_name":"Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus","publication_year":2020,"publication_date":"2020-04-20","ids":{"openalex":"https://openalex.org/W3003604522","doi":"https://doi.org/10.1145/3366423.3380270","mag":"3003604522"},"language":"en","primary_location":{"id":"doi:10.1145/3366423.3380270","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380270","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3366423.3380270","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Bang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Bang Liu","raw_affiliation_strings":["University of Alberta"],"affiliations":[{"raw_affiliation_string":"University of Alberta","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haojie Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haojie Wei","raw_affiliation_strings":["Tencent"],"affiliations":[{"raw_affiliation_string":"Tencent","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Di Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Di Niu","raw_affiliation_strings":["University of Alberta"],"affiliations":[{"raw_affiliation_string":"University of Alberta","institution_ids":["https://openalex.org/I154425047"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haolan Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolan Chen","raw_affiliation_strings":["Tencent"],"affiliations":[{"raw_affiliation_string":"Tencent","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yancheng He","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yancheng He","raw_affiliation_strings":["Tencent"],"affiliations":[{"raw_affiliation_string":"Tencent","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154425047"],"apc_list":null,"apc_paid":null,"fwci":3.7026,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.94196598,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2032","last_page":"2043"},"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":0.9973999857902527,"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":0.9915000200271606,"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/ask-price","display_name":"Ask price","score":0.7042999863624573},{"id":"https://openalex.org/keywords/chatbot","display_name":"Chatbot","score":0.6431000232696533},{"id":"https://openalex.org/keywords/text-generation","display_name":"Text generation","score":0.5993000268936157},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5378000140190125},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.5171999931335449},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.49779999256134033},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.45989999175071716},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.43050000071525574}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961000204086304},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.7042999863624573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6486999988555908},{"id":"https://openalex.org/C2779041454","wikidata":"https://www.wikidata.org/wiki/Q870780","display_name":"Chatbot","level":2,"score":0.6431000232696533},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.5993000268936157},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5597000122070312},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.5171999931335449},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.49779999256134033},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.45989999175071716},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C2474386","wikidata":"https://www.wikidata.org/wiki/Q461183","display_name":"Text corpus","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3276999890804291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25769999623298645},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3366423.3380270","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380270","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2002.00748","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2002.00748","pdf_url":"https://arxiv.org/pdf/2002.00748","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":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3366423.3380270","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3366423.3380270","pdf_url":null,"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 Web Conference 2020","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W2081580037","https://openalex.org/W2133459682","https://openalex.org/W2137006453","https://openalex.org/W2250539671","https://openalex.org/W2296073425","https://openalex.org/W2803595284","https://openalex.org/W2804292122","https://openalex.org/W2804340650","https://openalex.org/W2889670144","https://openalex.org/W2890166583","https://openalex.org/W2891946694","https://openalex.org/W2913710968","https://openalex.org/W2962944953","https://openalex.org/W2963323070","https://openalex.org/W2963661590","https://openalex.org/W2963748441","https://openalex.org/W2964236999","https://openalex.org/W2964309167","https://openalex.org/W2964912036","https://openalex.org/W2970250435","https://openalex.org/W2970705401","https://openalex.org/W2970796366","https://openalex.org/W2972324944"],"related_works":[],"abstract_inverted_index":{"The":[0,181],"ability":[1],"to":[2,14,31,123,168,172],"ask":[3,15],"questions":[4,16],"is":[5,67],"important":[6],"in":[7,195,204,230],"both":[8],"human":[9,102,170],"and":[10,22,27,61,87,134,142,166],"machine":[11,23],"intelligence.":[12],"Learning":[13],"helps":[17,28],"knowledge":[18],"acquisition,":[19],"improves":[20],"question-answering":[21],"reading":[24],"comprehension":[25],"tasks,":[26],"a":[29,37,47,69,101,144,211,226],"chatbot":[30],"keep":[32],"the":[33,99,116,138,174,177,198,205],"conversation":[34],"flowing":[35],"with":[36,163],"human.":[38],"Existing":[39],"question":[40,65,125,129,160,192],"generation":[41,66,161,193,199],"models":[42,162,194,208],"are":[43],"ineffective":[44],"at":[45,83,95],"generating":[46,85],"large":[48],"amount":[49,214],"of":[50,120,176,197,215],"high-quality":[51,86],"question-answer":[52,89,179,223],"pairs":[53,90,224],"from":[54,91,115,225],"unstructured":[55],"text,":[56],"since":[57],"given":[58],"an":[59,62,110],"answer":[60],"input":[63],"passage,":[64],"inherently":[68],"one-to-many":[70],"mapping.":[71],"In":[72],"this":[73],"paper,":[74],"we":[75,217],"propose":[76],"Answer-Clue-Style-aware":[77],"Question":[78],"Generation":[79],"(ACS-QG),":[80],"which":[81,113,131,148],"aims":[82],"automatically":[84],"diverse":[88,133],"unlabeled":[92],"text":[93,117,155],"corpus":[94],"scale":[96],"by":[97],"imitating":[98],"way":[100],"asks":[103],"questions.":[104],"Our":[105],"system":[106,187],"consists":[107],"of:":[108],"i)":[109],"information":[111,122],"extractor,":[112],"samples":[114],"multiple":[118],"types":[119],"assistive":[121,140],"guide":[124],"generation;":[126],"ii)":[127],"neural":[128,145,191],"generators,":[130],"generate":[132,219],"controllable":[135],"questions,":[136],"leveraging":[137],"extracted":[139],"information;":[141],"iii)":[143],"quality":[146,175],"controller,":[147],"removes":[149],"low-quality":[150],"generated":[151,178],"data":[152],"based":[153],"on":[154,210],"entailment.":[156],"We":[157],"compare":[158],"our":[159,186],"existing":[164],"approaches":[165],"resort":[167],"voluntary":[169],"evaluation":[171,182],"assess":[173],"pairs.":[180],"results":[183],"suggest":[184],"that":[185],"dramatically":[188],"outperforms":[189],"state-of-the-art":[190],"terms":[196],"quality,":[200],"while":[201],"being":[202],"scalable":[203],"meantime.":[206],"With":[207],"trained":[209],"relatively":[212],"smaller":[213],"data,":[216],"can":[218],"2.8":[220],"million":[221,227],"quality-assured":[222],"sentences":[228],"found":[229],"Wikipedia.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":6}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-02-07T00:00:00"}
