{"id":"https://openalex.org/W7129066919","doi":"https://doi.org/10.1145/3773966.3777938","title":"TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions","display_name":"TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129066919","doi":"https://doi.org/10.1145/3773966.3777938"},"language":null,"primary_location":{"id":"doi:10.1145/3773966.3777938","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777938","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 Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3773966.3777938","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064852810","display_name":"Abdelrahman Abdallah","orcid":"https://orcid.org/0000-0001-8747-4927"},"institutions":[{"id":"https://openalex.org/I190249584","display_name":"Universit\u00e4t Innsbruck","ror":"https://ror.org/054pv6659","country_code":"AT","type":"education","lineage":["https://openalex.org/I190249584"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Abdelrahman Abdallah","raw_affiliation_strings":["University of Innsbruck, Innsbruck, Austria"],"affiliations":[{"raw_affiliation_string":"University of Innsbruck, Innsbruck, Austria","institution_ids":["https://openalex.org/I190249584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008604727","display_name":"Bhawna Piryani","orcid":null},"institutions":[{"id":"https://openalex.org/I190249584","display_name":"Universit\u00e4t Innsbruck","ror":"https://ror.org/054pv6659","country_code":"AT","type":"education","lineage":["https://openalex.org/I190249584"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Bhawna Piryani","raw_affiliation_strings":["University of Innsbruck, Innsbruck, Austria"],"affiliations":[{"raw_affiliation_string":"University of Innsbruck, Innsbruck, Austria","institution_ids":["https://openalex.org/I190249584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126081126","display_name":"Jonas Wallat","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136150","display_name":"L3S Research Center","ror":"https://ror.org/039t4wk02","country_code":"DE","type":"facility","lineage":["https://openalex.org/I114112103","https://openalex.org/I4210136150","https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jonas Wallat","raw_affiliation_strings":["L3S Research Center, Hannover, Germany"],"affiliations":[{"raw_affiliation_string":"L3S Research Center, Hannover, Germany","institution_ids":["https://openalex.org/I4210136150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075681290","display_name":"Avishek Anand","orcid":"https://orcid.org/0000-0002-0163-0739"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Avishek Anand","raw_affiliation_strings":["Delft University of Technology, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"Delft University of Technology, Delft, Netherlands","institution_ids":["https://openalex.org/I98358874"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103289195","display_name":"Adam Jatowt","orcid":null},"institutions":[{"id":"https://openalex.org/I190249584","display_name":"Universit\u00e4t Innsbruck","ror":"https://ror.org/054pv6659","country_code":"AT","type":"education","lineage":["https://openalex.org/I190249584"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Adam Jatowt","raw_affiliation_strings":["University of Innsbruck, Innsbruck, Austria"],"affiliations":[{"raw_affiliation_string":"University of Innsbruck, Innsbruck, Austria","institution_ids":["https://openalex.org/I190249584"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064852810"],"corresponding_institution_ids":["https://openalex.org/I190249584"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.80801663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9279000163078308,"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":0.9279000163078308,"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":0.021400000900030136,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.008700000122189522,"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/modularity","display_name":"Modularity (biology)","score":0.5454999804496765},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.49380001425743103},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45879998803138733},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44510000944137573},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4047999978065491},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.3828999996185303},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3278999924659729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544999718666077},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.5454999804496765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5446000099182129},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45879998803138733},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44510000944137573},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3278999924659729},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.28859999775886536},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2856999933719635},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3773966.3777938","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777938","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 Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3773966.3777938","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777938","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 Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2039297031","https://openalex.org/W2077992594","https://openalex.org/W2080133951","https://openalex.org/W2083150957","https://openalex.org/W2137640420","https://openalex.org/W2185599447","https://openalex.org/W2912817604","https://openalex.org/W2963341956","https://openalex.org/W2970168256","https://openalex.org/W3015585801","https://openalex.org/W3021397474","https://openalex.org/W3099700870","https://openalex.org/W3154670582","https://openalex.org/W3176757281","https://openalex.org/W3200796517","https://openalex.org/W3206455169","https://openalex.org/W3217305727","https://openalex.org/W4212964822","https://openalex.org/W4226086513","https://openalex.org/W4244762921","https://openalex.org/W4283793506","https://openalex.org/W4284691483","https://openalex.org/W4287111051","https://openalex.org/W4306317226","https://openalex.org/W4384652647","https://openalex.org/W4385565111","https://openalex.org/W4385571357","https://openalex.org/W4385782731","https://openalex.org/W4386566778","https://openalex.org/W4389520172","https://openalex.org/W4392384332","https://openalex.org/W4393248081","https://openalex.org/W4396722687","https://openalex.org/W4402671909","https://openalex.org/W4402684293","https://openalex.org/W4403577942","https://openalex.org/W4405107150","https://openalex.org/W4412673557","https://openalex.org/W4412888159","https://openalex.org/W4412945025"],"related_works":[],"abstract_inverted_index":{"Temporal":[0],"information":[1,5,35],"is":[2],"crucial":[3],"for":[4],"retrieval,":[6],"yet":[7],"most":[8],"dense":[9,37,57],"retrieval":[10,39,138],"systems":[11],"focus":[12],"exclusively":[13],"on":[14,100,124,129],"semantic":[15,61],"similarity":[16],"while":[17],"neglecting":[18],"temporal":[19,34,64,84,93,102,137,156],"alignment":[20],"between":[21],"queries":[22],"and":[23,76,86,127,145,162,168,178],"documents.":[24],"We":[25,97],"propose":[26],"TempRetriever,":[27],"a":[28,82],"lightweight":[29],"framework":[30],"that":[31],"explicitly":[32],"incorporates":[33],"into":[36],"passage":[38],"through":[40],"learned":[41,83],"fusion":[42,68,151],"techniques.":[43],"Unlike":[44],"existing":[45,155],"approaches":[46],"requiring":[47],"extensive":[48],"architectural":[49],"modifications":[50],"or":[51],"specialized":[52],"pre-training,":[53],"TempRetriever":[54,99,116],"enhances":[55],"standard":[56,121],"retrievers":[58],"by":[59,160,164],"combining":[60],"embeddings":[62],"with":[63,180],"representations":[65],"using":[66],"four":[67],"strategies:":[69],"Feature":[70],"Stacking,":[71],"Vector":[72],"Summation,":[73],"Relative":[74],"Embeddings,":[75],"Element-Wise":[77],"Interaction.":[78],"Our":[79,132],"approach":[80],"introduces":[81],"encoder":[85],"time-based":[87],"negative":[88],"sampling":[89],"strategy":[90],"to":[91,114],"address":[92],"misalignment":[94],"during":[95],"training.":[96],"evaluate":[98],"three":[101],"question":[103],"answering":[104],"datasets":[105],"(ArchivalQA,":[106],"ChroniclingAmericaQA,":[107],"NobelPrize)":[108],"spanning":[109],"altogether":[110],"years":[111],"from":[112],"1800":[113],"2022.":[115],"achieves":[117],"substantial":[118],"improvements":[119],"over":[120,143,147],"DPR:":[122],"6.86%":[123],"ArchivalQA":[125],"(Recall@1)":[126],"4.40%":[128],"ChroniclingAmericaQA":[130],"(Recall@1).":[131],"method":[133],"also":[134],"outperforms":[135],"state-of-the-art":[136],"systems,":[139],"obtaining":[140],"9.62%":[141],"improvement":[142],"BiTimeBERT":[144,159],"5.16%":[146],"TS-Retriever.":[148],"Notably,":[149],"TempRetriever's":[150],"techniques":[152],"can":[153],"enhance":[154],"methods,":[157],"improving":[158],"5.12%":[161],"TS-Retriever":[163],"6.17%,":[165],"demonstrating":[166],"modularity":[167],"practical":[169],"value.":[170],"Zero-shot":[171],"evaluation":[172],"confirms":[173],"strong":[174],"generalization":[175],"across":[176],"domains,":[177],"integration":[179],"retrieval-augmented":[181],"generation":[182],"shows":[183],"consistent":[184],"end-to-end":[185],"improvements.":[186]},"counts_by_year":[],"updated_date":"2026-02-18T06:20:13.636215","created_date":"2026-02-17T00:00:00"}
