{"id":"https://openalex.org/W4365441107","doi":"https://doi.org/10.1145/3543873.3584621","title":"Explicit and Implicit Semantic Ranking Framework","display_name":"Explicit and Implicit Semantic Ranking Framework","publication_year":2023,"publication_date":"2023-04-28","ids":{"openalex":"https://openalex.org/W4365441107","doi":"https://doi.org/10.1145/3543873.3584621"},"language":"en","primary_location":{"id":"doi:10.1145/3543873.3584621","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543873.3584621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2304.04918","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102940930","display_name":"Xiaofeng Zhu","orcid":"https://orcid.org/0000-0001-7631-221X"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaofeng Zhu","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089009363","display_name":"Thomas Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Lin","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101715411","display_name":"Vishal Anand","orcid":"https://orcid.org/0000-0001-9632-6934"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishal Anand","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074933147","display_name":"Matthew D. Calderwood","orcid":"https://orcid.org/0000-0002-1314-3321"},"institutions":[{"id":"https://openalex.org/I4210125787","display_name":"Nuance Communications (United States)","ror":"https://ror.org/0311h6702","country_code":"US","type":"company","lineage":["https://openalex.org/I4210125787"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Calderwood","raw_affiliation_strings":["Nuance Communications, USA"],"affiliations":[{"raw_affiliation_string":"Nuance Communications, USA","institution_ids":["https://openalex.org/I4210125787"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027880133","display_name":"Eric Clausen-Brown","orcid":"https://orcid.org/0000-0003-0260-3561"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Clausen-Brown","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017350695","display_name":"Gord Lueck","orcid":"https://orcid.org/0000-0002-6176-3568"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gord Lueck","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027015945","display_name":"Wen-wai Yim","orcid":"https://orcid.org/0000-0001-9011-0817"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wen-Wai Yim","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069672506","display_name":"Cheng Ru Wu","orcid":"https://orcid.org/0000-0002-3351-840X"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng Wu","raw_affiliation_strings":["Microsoft Corporation, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Corporation, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5102940930"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":0.3479,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63417825,"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":"326","last_page":"330"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T11719","display_name":"Data Quality and Management","score":0.9961000084877014,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9908999800682068,"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":0.8384157419204712},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.5855469703674316},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5436044335365295},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.46368637681007385},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.447528213262558},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4273255467414856},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.424618661403656},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4177710711956024},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4019482135772705},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.36829763650894165}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8384157419204712},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.5855469703674316},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5436044335365295},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.46368637681007385},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.447528213262558},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4273255467414856},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.424618661403656},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4177710711956024},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4019482135772705},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.36829763650894165},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3543873.3584621","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543873.3584621","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2023","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2304.04918","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.04918","pdf_url":"https://arxiv.org/pdf/2304.04918","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":"pmh:oai:arXiv.org:2304.04918","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2304.04918","pdf_url":"https://arxiv.org/pdf/2304.04918","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":[{"display_name":"Industry, innovation and infrastructure","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4365441107.pdf","grobid_xml":"https://content.openalex.org/works/W4365441107.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W2059001985","https://openalex.org/W2091158010","https://openalex.org/W2108862644","https://openalex.org/W2115584760","https://openalex.org/W2136189984","https://openalex.org/W2143331230","https://openalex.org/W2154652894","https://openalex.org/W2155211665","https://openalex.org/W2186845332","https://openalex.org/W2373570000","https://openalex.org/W2898073868","https://openalex.org/W2950444459","https://openalex.org/W2963341956","https://openalex.org/W2963775347","https://openalex.org/W2978017171","https://openalex.org/W2984064077","https://openalex.org/W3000163445","https://openalex.org/W3017018726","https://openalex.org/W3021244424","https://openalex.org/W3021397474","https://openalex.org/W3034439313","https://openalex.org/W3045635560","https://openalex.org/W3099700870","https://openalex.org/W3102066094","https://openalex.org/W3105817677","https://openalex.org/W3105949871","https://openalex.org/W3172806051","https://openalex.org/W4206390054","https://openalex.org/W4252076394","https://openalex.org/W4287704453","https://openalex.org/W4287887173","https://openalex.org/W4378513414","https://openalex.org/W4385573581","https://openalex.org/W6781533629"],"related_works":["https://openalex.org/W402673672","https://openalex.org/W3127142483","https://openalex.org/W4385565564","https://openalex.org/W2138488530","https://openalex.org/W2898073868","https://openalex.org/W2798835721","https://openalex.org/W2971071571","https://openalex.org/W2387658907","https://openalex.org/W2922169395","https://openalex.org/W2385796165"],"abstract_inverted_index":{"The":[0],"core":[1],"challenge":[2],"in":[3,130,148,151,158,198],"numerous":[4],"real-world":[5,89],"applications":[6],"is":[7],"to":[8,12,79,180],"match":[9],"an":[10],"inquiry":[11],"the":[13,113,135,139,162],"best":[14,114],"document":[15],"from":[16,116],"a":[17,45,176],"mutable":[18,63],"and":[19,67,71,74,96,122,142],"finite":[20],"set":[21],"of":[22],"candidates.":[23],"Existing":[24],"industry":[25,84],"solutions,":[26],"especially":[27],"latency-constrained":[28],"services,":[29],"often":[30],"rely":[31],"on":[32,82,120,134],"similarity":[33],"algorithms":[34],"that":[35,171],"sacrifice":[36],"quality":[37,69,183],"for":[38,175],"speed.":[39],"In":[40,101,161],"this":[41],"paper":[42],"we":[43],"introduce":[44],"generic":[46],"semantic":[47],"learning-to-rank":[48],"framework,":[49],"Self-training":[50],"Semantic":[51],"Cross-attention":[52],"Ranking":[53],"(sRank).":[54],"This":[55],"transformer-based":[56],"framework":[57],"uses":[58],"linear":[59],"pairwise":[60],"loss":[61],"with":[62,108,193],"training":[64],"batch":[65],"sizes":[66],"achieves":[68,127,187],"gains":[70,81],"high":[72],"efficiency,":[73],"has":[75,143],"been":[76],"applied":[77],"effectively":[78],"show":[80],"two":[83],"tasks":[85],"at":[86],"Microsoft":[87],"over":[88,138],"large-scale":[90],"data":[91],"sets:":[92],"Smart":[93,102],"Reply":[94],"(SR)":[95],"Ambient":[97],"Clinical":[98],"Intelligence":[99],"(ACI).":[100],"Reply,":[103],"sRank":[104,165],"assists":[105],"live":[106],"customers":[107],"technical":[109],"support":[110,123],"by":[111],"selecting":[112],"reply":[115],"predefined":[117],"solutions":[118],"based":[119],"consumer":[121],"agent":[124],"messages.":[125],"It":[126,186],"11.7%":[128],"gain":[129,197],"offline":[131],"top-one":[132,189],"accuracy":[133,190],"SR":[136],"task":[137],"previous":[140],"system,":[141],"enabled":[144],"38.7%":[145],"time":[146],"reduction":[147],"composing":[149],"messages":[150],"telemetry":[152],"recorded":[153],"since":[154],"its":[155],"general":[156],"release":[157],"January":[159],"2021.":[160],"ACI":[163],"task,":[164],"selects":[166],"relevant":[167],"historical":[168],"physician":[169],"templates":[170],"serve":[172],"as":[173],"guidance":[174],"text":[177],"summarization":[178],"model":[179],"generate":[181],"higher":[182],"medical":[184,200],"notes.":[185,201],"35.5%":[188],"gain,":[191],"along":[192],"46%":[194],"relative":[195],"ROUGE-L":[196],"generated":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
