{"id":"https://openalex.org/W4416034547","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1290","title":"Mitigating Spurious Correlations via Counterfactual Contrastive Learning","display_name":"Mitigating Spurious Correlations via Counterfactual Contrastive Learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416034547","doi":"https://doi.org/10.18653/v1/2025.findings-emnlp.1290"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1290","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1290","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1290.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-emnlp.1290.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119679554","display_name":"Fengxiang Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Fengxiang Cheng","raw_affiliation_strings":["University of Amsterdam ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam ,","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013798538","display_name":"Chuan Zhou","orcid":"https://orcid.org/0000-0002-6162-9748"},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Chuan Zhou","raw_affiliation_strings":["The University of Melbourne , 3 MBZUAI ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne , 3 MBZUAI ,","institution_ids":["https://openalex.org/I4210113480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100745282","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-5089-4538"},"institutions":[{"id":"https://openalex.org/I111483173","display_name":"King University","ror":"https://ror.org/01evb6z23","country_code":"US","type":"education","lineage":["https://openalex.org/I111483173"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["Peking University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University ,","institution_ids":["https://openalex.org/I111483173","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042804534","display_name":"Alina Leidinger","orcid":"https://orcid.org/0000-0003-2648-6646"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Alina Leidinger","raw_affiliation_strings":["University of Amsterdam ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam ,","institution_ids":["https://openalex.org/I887064364"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100629471","display_name":"Haoxuan Li","orcid":"https://orcid.org/0009-0007-9923-5481"},"institutions":[{"id":"https://openalex.org/I111483173","display_name":"King University","ror":"https://ror.org/01evb6z23","country_code":"US","type":"education","lineage":["https://openalex.org/I111483173"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Haoxuan Li","raw_affiliation_strings":["Peking University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University ,","institution_ids":["https://openalex.org/I111483173","https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102023771","display_name":"Mingming Gong","orcid":"https://orcid.org/0000-0001-7147-5589"},"institutions":[{"id":"https://openalex.org/I4210113480","display_name":"Mohamed bin Zayed University of Artificial Intelligence","ror":"https://ror.org/0258gkt32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210113480"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Mingming Gong","raw_affiliation_strings":["The University of Melbourne , 3 MBZUAI ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Melbourne , 3 MBZUAI ,","institution_ids":["https://openalex.org/I4210113480"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083671376","display_name":"Fenrong Liu","orcid":"https://orcid.org/0000-0003-1978-5306"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fenrong Liu","raw_affiliation_strings":["Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022180773","display_name":"Robert van Rooij","orcid":"https://orcid.org/0000-0001-6819-6685"},"institutions":[{"id":"https://openalex.org/I887064364","display_name":"University of Amsterdam","ror":"https://ror.org/04dkp9463","country_code":"NL","type":"education","lineage":["https://openalex.org/I887064364"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Robert Van Rooij","raw_affiliation_strings":["University of Amsterdam ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Amsterdam ,","institution_ids":["https://openalex.org/I887064364"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28306314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"23713","last_page":"23722"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.4453999996185303,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.4453999996185303,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07169999927282333,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06960000097751617,"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/spurious-relationship","display_name":"Spurious relationship","score":0.7522000074386597},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.7008000016212463},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3142000138759613},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.25589999556541443}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.7522000074386597},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.7008000016212463},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5134000182151794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48969998955726624},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3248000144958496},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.28360000252723694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C2777629044","wikidata":"https://www.wikidata.org/wiki/Q614959","display_name":"Contrastive analysis","level":2,"score":0.24779999256134033}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-emnlp.1290","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1290","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1290.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-emnlp.1290","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-emnlp.1290","pdf_url":"https://aclanthology.org/2025.findings-emnlp.1290.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EMNLP 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2138920778","display_name":null,"funder_award_id":"623B2002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6547754354","display_name":null,"funder_award_id":"406.18","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"},{"id":"https://openalex.org/G6880877711","display_name":"From Learning to Meaning: A new approach to Generic Sentences and Implicit Biases","funder_award_id":"406.18.TW.007","funder_id":"https://openalex.org/F4320321800","funder_display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"},{"id":"https://openalex.org/F4320322392","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416034547.pdf","grobid_xml":"https://content.openalex.org/works/W4416034547.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Identifying":[0],"causal":[1,24,46,63,75],"relationships":[2],"rather":[3],"than":[4],"spurious":[5],"correlations":[6],"between":[7],"words":[8,28,43,58,82,172,184],"and":[9,36,101,143,149,155,176,182],"class":[10],"labels":[11],"plays":[12],"a":[13,52,114,117,124,191],"crucial":[14],"role":[15],"in":[16,78,90,120,130],"building":[17,38],"robust":[18,39,164,201],"text":[19,40,165],"classifiers.Previous":[20],"studies":[21],"proposed":[22,198],"using":[23,42],"effects":[25,64],"to":[26,33,107,199,210],"distinguish":[27],"that":[29,50,112,134],"are":[30,205],"causally":[31,56,80,179],"related":[32,57,81,180],"the":[34,60,71,96,109,121,127,131,139,151,158,163,171,212],"sentiment,":[35],"then":[37],"classifiers":[41],"with":[44,173],"high":[45],"effects.However,":[47],"we":[48,93,136],"find":[49],"when":[51],"sentence":[53,115],"has":[54,116],"multiple":[55],"simultaneously,":[59],"magnitude":[61],"of":[62,73,98,103,123,133,141,153,214],"will":[65],"be":[66],"significantly":[67],"reduced,":[68],"which":[69],"limits":[70],"applicability":[72],"previous":[74],"effectbased":[76],"methods":[77],"distinguishing":[79],"from":[83],"spuriously":[84,186],"correlated":[85,187],"ones.To":[86],"fill":[87],"this":[88,91],"gap,":[89],"paper,":[92],"introduce":[94],"both":[95],"probability":[97,102],"necessity":[99],"(PN)":[100],"sufficiency":[104],"(PS),":[105],"aiming":[106],"answer":[108],"counterfactual":[110],"question":[111],"'if":[113],"certain":[118],"sentiment":[119,128,147,202],"presence/absence":[122],"word,":[125],"would":[126],"change":[129],"absence/presence":[132],"word?'.Specifically,":[135],"first":[137],"derive":[138],"identifiability":[140],"PN":[142,154,175],"PS":[144,156,177],"under":[145],"different":[146],"monotonicities,":[148],"calibrate":[150],"estimation":[152],"via":[157],"estimated":[159],"average":[160],"treatment":[161],"effect.Finally,":[162],"classifier":[166],"is":[167,197],"built":[168],"by":[169],"identifying":[170],"larger":[174],"as":[178,185],"words,":[181,188],"other":[183],"based":[189],"on":[190,207],"contrastive":[192],"learning":[193],"approach":[194],"name":[195],"CPNS":[196],"achieve":[200],"classification.Extensive":[203],"experiments":[204],"conducted":[206],"public":[208],"datasets":[209],"validate":[211],"effectiveness":[213],"our":[215],"method.":[216]},"counts_by_year":[],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2025-11-08T00:00:00"}
