{"id":"https://openalex.org/W2950729111","doi":"https://doi.org/10.18653/v1/p19-1436","title":"Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index","display_name":"Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2950729111","doi":"https://doi.org/10.18653/v1/p19-1436","mag":"2950729111"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1436","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1436","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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/p19-1436","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104331009","display_name":"Minjoon Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Minjoon Seo","raw_affiliation_strings":["Google Research","Korea University"],"affiliations":[{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Korea University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002413587","display_name":"Jinhyuk Lee","orcid":"https://orcid.org/0000-0003-4972-239X"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinhyuk Lee","raw_affiliation_strings":["University of Washington"],"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011150187","display_name":"Tom Kwiatkowski","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140341","display_name":"Allen Institute","ror":"https://ror.org/03cpe7c52","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210140341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tom Kwiatkowski","raw_affiliation_strings":["Allen Institute for AI"],"affiliations":[{"raw_affiliation_string":"Allen Institute for AI","institution_ids":["https://openalex.org/I4210140341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109937799","display_name":"Ankur P. Parikh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210140341","display_name":"Allen Institute","ror":"https://ror.org/03cpe7c52","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210140341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankur Parikh","raw_affiliation_strings":["Allen Institute for AI"],"affiliations":[{"raw_affiliation_string":"Allen Institute for AI","institution_ids":["https://openalex.org/I4210140341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101576595","display_name":"Ali Farhadi","orcid":"https://orcid.org/0000-0001-7249-2380"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I4210107081","display_name":"Xenobe Research Institute","ror":"https://ror.org/01pb5g963","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210107081"]},{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Ali Farhadi","raw_affiliation_strings":["XNOR.AI","Clova AI, NAVER","Google Research"],"affiliations":[{"raw_affiliation_string":"XNOR.AI","institution_ids":["https://openalex.org/I4210107081"]},{"raw_affiliation_string":"Clova AI, NAVER","institution_ids":["https://openalex.org/I60922564"]},{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082305994","display_name":"Hannaneh Hajishirzi","orcid":"https://orcid.org/0000-0002-1055-6657"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I4210107081","display_name":"Xenobe Research Institute","ror":"https://ror.org/01pb5g963","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210107081"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hannaneh Hajishirzi","raw_affiliation_strings":["XNOR.AI","Google Research"],"affiliations":[{"raw_affiliation_string":"XNOR.AI","institution_ids":["https://openalex.org/I4210107081"]},{"raw_affiliation_string":"Google Research","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5104331009"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":14.0322,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.99074074,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4430","last_page":"4441"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9983000159263611,"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":0.9113268256187439},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7051733136177063},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6526293754577637},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.594996452331543},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5819642543792725},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.5764821171760559},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5721567273139954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.544644832611084},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5439586043357849},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5423032641410828},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.47391948103904724},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4415700137615204},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.26709070801734924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9113268256187439},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7051733136177063},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6526293754577637},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.594996452331543},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5819642543792725},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.5764821171760559},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5721567273139954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.544644832611084},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5439586043357849},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5423032641410828},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.47391948103904724},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4415700137615204},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.26709070801734924},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-1436","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1436","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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1436","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1436","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 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6200000047683716,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1502916507","https://openalex.org/W1694079474","https://openalex.org/W1880262756","https://openalex.org/W2017851434","https://openalex.org/W2028175314","https://openalex.org/W2094728533","https://openalex.org/W2124509324","https://openalex.org/W2131965512","https://openalex.org/W2147152072","https://openalex.org/W2169054943","https://openalex.org/W2251818205","https://openalex.org/W2252136820","https://openalex.org/W2475334473","https://openalex.org/W2521709538","https://openalex.org/W2551396370","https://openalex.org/W2593864460","https://openalex.org/W2750557179","https://openalex.org/W2769395616","https://openalex.org/W2788448041","https://openalex.org/W2799081691","https://openalex.org/W2889181558","https://openalex.org/W2889729765","https://openalex.org/W2896457183","https://openalex.org/W2912817604","https://openalex.org/W2913222130","https://openalex.org/W2949428332","https://openalex.org/W2962739339","https://openalex.org/W2962881743","https://openalex.org/W2962985038","https://openalex.org/W2963047838","https://openalex.org/W2963056065","https://openalex.org/W2963159735","https://openalex.org/W2963195889","https://openalex.org/W2963341956","https://openalex.org/W2963448850","https://openalex.org/W2963469388","https://openalex.org/W2963748441","https://openalex.org/W2963967365","https://openalex.org/W3104916409","https://openalex.org/W3121694563","https://openalex.org/W4293004632","https://openalex.org/W4299585995","https://openalex.org/W4300687121"],"related_works":["https://openalex.org/W2368542989","https://openalex.org/W207304934","https://openalex.org/W2964061310","https://openalex.org/W2086064646","https://openalex.org/W4309395021","https://openalex.org/W2798526799","https://openalex.org/W2020540721","https://openalex.org/W2809851383","https://openalex.org/W2369308426","https://openalex.org/W2949992439"],"abstract_inverted_index":{"Existing":[0],"open-domain":[1,45],"question":[2],"answering":[3],"(QA)":[4],"models":[5,138],"are":[6,159],"not":[7],"suitable":[8],"for":[9,21,73],"real-time":[10],"usage":[11],"because":[12],"they":[13],"need":[14],"to":[15,98,114],"process":[16],"several":[17],"long":[18],"documents":[19],"on-demand":[20],"every":[22],"input":[23],"query,":[24],"which":[25,144],"is":[26,129],"computationally":[27],"prohibitive.":[28],"In":[29,47],"this":[30],"paper,":[31],"we":[32],"introduce":[33],"query-agnostic":[34],"indexable":[35],"representations":[36],"of":[37,60,68],"document":[38],"phrases":[39,62,95,107],"that":[40,126],"can":[41,81],"drastically":[42],"speed":[43],"up":[44],"QA.":[46],"particular,":[48],"our":[49,79,104,127],"dense-sparse":[50],"phrase":[51],"encoding":[52],"effectively":[53],"captures":[54],"syntactic,":[55],"semantic,":[56],"and":[57,63,76,84,101,157],"lexical":[58],"information":[59],"the":[61,65,109],"eliminates":[64],"pipeline":[66],"filtering":[67],"context":[69],"documents.":[70],"Leveraging":[71],"strategies":[72],"optimizing":[74],"training":[75],"inference":[77,152],"time,":[78],"model":[80,105,128],"be":[82],"trained":[83],"deployed":[85],"even":[86],"in":[87,108],"a":[88],"single":[89],"4-GPU":[90],"server.":[91],"Moreover,":[92],"by":[93],"representing":[94],"as":[96],"pointers":[97],"their":[99],"start":[100],"end":[102],"tokens,":[103],"indexes":[106],"entire":[110],"English":[111],"Wikipedia":[112],"(up":[113],"60":[115],"billion":[116],"phrases)":[117],"using":[118],"under":[119],"2TB.":[120],"Our":[121],"experiments":[122],"on":[123,130,154],"SQuAD-Open":[124],"show":[125],"par":[131],"with":[132,139],"or":[133],"more":[134],"accurate":[135],"than":[136],"previous":[137],"6000x":[140],"reduced":[141],"computational":[142],"cost,":[143],"translates":[145],"into":[146],"at":[147,161],"least":[148],"68x":[149],"faster":[150],"end-to-end":[151],"benchmark":[153],"CPUs.":[155],"Code":[156],"demo":[158],"available":[160],"nlp.cs.washington.edu/denspi":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":36},{"year":2020,"cited_by_count":37},{"year":2019,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}