{"id":"https://openalex.org/W4225103652","doi":"https://doi.org/10.1145/3477495.3531932","title":"Adaptable Text Matching via Meta-Weight Regulator","display_name":"Adaptable Text Matching via Meta-Weight Regulator","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4225103652","doi":"https://doi.org/10.1145/3477495.3531932"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531932","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531932","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th 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/2204.12668","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100335260","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-2289-2877"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115595935","display_name":"Chen Zhang","orcid":"https://orcid.org/0000-0003-0988-8723"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zhang","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114140127","display_name":"Fang Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Ma","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050532724","display_name":"Dawei Song","orcid":"https://orcid.org/0000-0002-8660-3608"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Song","raw_affiliation_strings":["Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0265201,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"870","last_page":"879"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9936000108718872,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9894999861717224,"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":0.8198975324630737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6653164625167847},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6239977478981018},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5894320607185364},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5476585030555725},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.52912437915802},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5214771032333374},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.49987292289733887},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.45782244205474854},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.44294852018356323},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4175228774547577},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4155111014842987},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3603709936141968}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8198975324630737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6653164625167847},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6239977478981018},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5894320607185364},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5476585030555725},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.52912437915802},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5214771032333374},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.49987292289733887},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.45782244205474854},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.44294852018356323},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4175228774547577},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4155111014842987},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3603709936141968},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3477495.3531932","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531932","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:oro.open.ac.uk:83719","is_oa":false,"landing_page_url":"https://oro.open.ac.uk/83719/1/Adaptable_Text_Matching_via_Meta_Weight_Regulator__raw_version_.pdf","pdf_url":"https://oro.open.ac.uk/83719/1/Adaptable_Text_Matching_via_Meta_Weight_Regulator__raw_version_.pdf","source":{"id":"https://openalex.org/S4377196284","display_name":"Open Research Online - ORO (The Open University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204136569","host_organization_name":"The Open University","host_organization_lineage":["https://openalex.org/I204136569"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"},{"id":"pmh:oai:arXiv.org:2204.12668","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.12668","pdf_url":"https://arxiv.org/pdf/2204.12668","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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:2204.12668","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.12668","pdf_url":"https://arxiv.org/pdf/2204.12668","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1988790447","https://openalex.org/W1989684337","https://openalex.org/W2063471322","https://openalex.org/W2132984949","https://openalex.org/W2251818205","https://openalex.org/W2286300105","https://openalex.org/W2296073425","https://openalex.org/W2508865106","https://openalex.org/W2618735189","https://openalex.org/W2788496822","https://openalex.org/W2945801692","https://openalex.org/W2962992635","https://openalex.org/W2963351448","https://openalex.org/W2964154091","https://openalex.org/W2979826702","https://openalex.org/W2997064279","https://openalex.org/W3035294798","https://openalex.org/W4212774754","https://openalex.org/W4288616731","https://openalex.org/W6679390333"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W2250140200","https://openalex.org/W3121791438"],"abstract_inverted_index":{"Neural":[0],"text":[1,230,267],"matching":[2,231,268],"models":[3,29,269],"have":[4,21,85],"been":[5],"used":[6,192,236],"in":[7,36,39,59,63,200,270],"a":[8,23,32,37,46,51,69,77,88,97,106,120,126,170,176,201,250],"range":[9],"of":[10,31,72,162,197,252,264],"applications":[11],"such":[12],"as":[13],"question":[14],"answering":[15],"and":[16,20,158,238,256,261],"natural":[17],"language":[18],"inference,":[19],"yielded":[22],"good":[24],"performance.":[25,209],"However,":[26,95],"these":[27],"neural":[28,221,266],"are":[30,182,190,225],"limited":[33,70],"adaptability,":[34],"resulting":[35],"decline":[38],"performance":[40],"when":[41],"encountering":[42],"test":[43],"examples":[44,137,168],"from":[45],"different":[47,52],"dataset":[48,79,92,109],"or":[49,80,93,110],"even":[50],"task.":[53,94],"The":[54,241],"adaptability":[55,263],"is":[56,67,112,125,204,212],"particularly":[57],"important":[58],"the":[60,101,135,143,150,153,160,163,166,185,195,207,259,265,271],"few-shot":[61,107,272],"setting:":[62],"many":[64],"cases,":[65],"there":[66],"only":[68],"amount":[71],"labeled":[73,90],"data":[74,104],"available":[75],"for":[76],"target":[78,108,144,167,208],"task,":[81],"while":[82],"we":[83,118],"may":[84],"access":[86],"to":[87,105,131,134,142,184,193,206,218],"richly":[89],"source":[91,103,136,156,186,198],"adapting":[96],"model":[98,151,164],"trained":[99],"on":[100,139,152,165,233],"abundant":[102],"task":[111],"challenging.":[113],"To":[114],"tackle":[115],"this":[116],"challenge,":[117],"propose":[119],"Meta-Weight":[121],"Regulator":[122],"(MWR),":[123],"which":[124],"meta-learning":[127],"approach":[128,247],"that":[129,203,244],"learns":[130],"assign":[132],"weights":[133,196],"based":[138],"their":[140],"relevance":[141],"loss.":[145],"Specifically,":[146],"MWR":[147,211],"first":[148],"trains":[149],"uniformly":[154],"weighted":[155],"examples,":[157,199],"measures":[159],"efficacy":[161],"via":[169],"loss":[171],"function.":[172],"By":[173],"iteratively":[174],"performing":[175],"(meta)":[177],"gradient":[178],"descent,":[179],"high-order":[180],"gradients":[181,189],"propagated":[183],"examples.":[187],"These":[188],"then":[191],"update":[194],"way":[202],"relevant":[205],"As":[210],"model-agnostic,":[213],"it":[214],"can":[215],"be":[216],"applied":[217],"any":[219],"backbone":[220,229],"model.":[222],"Extensive":[223],"experiments":[224],"conducted":[226],"with":[227],"various":[228],"models,":[232],"four":[234],"widely":[235],"datasets":[237],"two":[239],"tasks.":[240],"results":[242],"demonstrate":[243],"our":[245],"proposed":[246],"significantly":[248],"outperforms":[249],"number":[251],"existing":[253],"adaptation":[254],"methods":[255],"effectively":[257],"improves":[258],"cross-dataset":[260],"cross-task":[262],"setting.":[273]},"counts_by_year":[],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}