{"id":"https://openalex.org/W3021244424","doi":"https://doi.org/10.1145/3397271.3401093","title":"Efficient Document Re-Ranking for Transformers by Precomputing Term Representations","display_name":"Efficient Document Re-Ranking for Transformers by Precomputing Term Representations","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3021244424","doi":"https://doi.org/10.1145/3397271.3401093","mag":"3021244424"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401093","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.14255","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sean MacAvaney","orcid":null},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sean MacAvaney","raw_affiliation_strings":["Georgetown University, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgetown University, Washington, DC, USA","institution_ids":["https://openalex.org/I184565670"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Franco Maria Nardini","orcid":null},"institutions":[{"id":"https://openalex.org/I122991210","display_name":"Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"","ror":"https://ror.org/05kacka20","country_code":"IT","type":"facility","lineage":["https://openalex.org/I122991210","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Franco Maria Nardini","raw_affiliation_strings":["ISTI-CNR, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ISTI-CNR, Pisa, Italy","institution_ids":["https://openalex.org/I122991210"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Raffaele Perego","orcid":null},"institutions":[{"id":"https://openalex.org/I122991210","display_name":"Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"","ror":"https://ror.org/05kacka20","country_code":"IT","type":"facility","lineage":["https://openalex.org/I122991210","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Raffaele Perego","raw_affiliation_strings":["ISTI-CNR, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ISTI-CNR, Pisa, Italy","institution_ids":["https://openalex.org/I122991210"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nicola Tonellotto","orcid":null},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicola Tonellotto","raw_affiliation_strings":["University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nazli Goharian","orcid":null},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nazli Goharian","raw_affiliation_strings":["Georgetown University, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgetown University, Washington, DC, USA","institution_ids":["https://openalex.org/I184565670"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ophir Frieder","orcid":null},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ophir Frieder","raw_affiliation_strings":["Georgetown University, Washington, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgetown University, Washington, DC, USA","institution_ids":["https://openalex.org/I184565670"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T11719","display_name":"Data Quality and Management","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/search-engine-indexing","display_name":"Search engine indexing","score":0.6883999705314636},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6208999752998352},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5863000154495239},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5853999853134155},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.4982999861240387},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4388999938964844},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.429500013589859},{"id":"https://openalex.org/keywords/automatic-indexing","display_name":"Automatic indexing","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7785999774932861},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.6883999705314636},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6208999752998352},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5863000154495239},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5853999853134155},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.48069998621940613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45190000534057617},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43810001015663147},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C2778330532","wikidata":"https://www.wikidata.org/wiki/Q4826577","display_name":"Automatic indexing","level":3,"score":0.3889999985694885},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3397271.3401093","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401093","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.14255","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.14255","pdf_url":"https://arxiv.org/pdf/2004.14255","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"},{"id":"pmh:oai:arpi.unipi.it:11568/1054901","is_oa":true,"landing_page_url":"http://hdl.handle.net/11568/1054901","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:dnet:people______::e4f3be493854775a85fdf7ba110291f8","is_oa":true,"landing_page_url":"https://openportal.isti.cnr.it/doc?id=people______::e4f3be493854775a85fdf7ba110291f8","pdf_url":null,"source":{"id":"https://openalex.org/S7407055261","display_name":"ISTI Open Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49\u201358, online, 25-30 July, 2020","raw_type":"http://purl.org/coar/resource_type/c_5794"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2004.14255","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.14255","pdf_url":"https://arxiv.org/pdf/2004.14255","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1685426458","https://openalex.org/W2000431947","https://openalex.org/W2096937925","https://openalex.org/W2566147423","https://openalex.org/W2610935556","https://openalex.org/W2616330167","https://openalex.org/W2740321901","https://openalex.org/W2886518140","https://openalex.org/W2897754576","https://openalex.org/W2912817604","https://openalex.org/W2945127593","https://openalex.org/W2955732934","https://openalex.org/W2964209691","https://openalex.org/W2970103342","https://openalex.org/W3001344098","https://openalex.org/W3001665736","https://openalex.org/W4205951122"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"pretrained":[1],"transformer":[2,43],"networks":[3,44,56],"are":[4],"effective":[5,110],"at":[6,75,88],"various":[7],"ranking":[8,64,95,146],"tasks,":[9],"such":[10],"as":[11],"question":[12],"answering":[13],"and":[14,81,136],"ad-hoc":[15],"document":[16,52,72],"ranking.":[17],"However,":[18],"their":[19],"computational":[20],"expenses":[21],"deem":[22],"them":[23,83],"cost-prohibitive":[24],"in":[25,61,145],"practice.":[26],"Our":[27,126],"proposed":[28],"approach,":[29],"called":[30],"PreTTR":[31],"(Precomputing":[32],"Transformer":[33],"Term":[34],"Representations),":[35],"considerably":[36],"reduces":[37,129],"the":[38,71,85,93,99,103,114,130],"query-time":[39],"latency":[40],"of":[41,70,102],"deep":[42],"(up":[45],"to":[46,59,91,98,112,122,134],"a":[47,62,79,119,142],"42x":[48],"speedup":[49],"on":[50],"web":[51],"ranking)":[53],"making":[54],"these":[55],"more":[57],"practical":[58],"use":[60],"real-time":[63],"scenario.":[65],"Specifically,":[66],"we":[67,106],"precompute":[68],"part":[69],"term":[73],"representations":[74],"indexing":[76],"time":[77,90],"(without":[78],"query),":[80],"merge":[82],"with":[84],"query":[86,89],"representation":[87],"compute":[92],"final":[94],"score.":[96],"Due":[97],"large":[100],"size":[101],"token":[104],"representations,":[105],"also":[107],"propose":[108],"an":[109],"approach":[111],"reduce":[113],"storage":[115,131],"requirement":[116],"by":[117],"training":[118],"compression":[120,127],"layer":[121],"match":[123],"attention":[124],"scores.":[125],"technique":[128],"required":[132],"up":[133],"95%":[135],"it":[137],"can":[138],"be":[139],"applied":[140],"without":[141],"substantial":[143],"degradation":[144],"performance.":[147]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2020-05-13T00:00:00"}
