{"id":"https://openalex.org/W4412889691","doi":"https://doi.org/10.18653/v1/2025.acl-long.1446","title":"Bilingual Zero-Shot Stance Detection","display_name":"Bilingual Zero-Shot Stance Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889691","doi":"https://doi.org/10.18653/v1/2025.acl-long.1446"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1446","pdf_url":"https://aclanthology.org/2025.acl-long.1446.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1446.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020192829","display_name":"Chenye Zhao","orcid":"https://orcid.org/0000-0002-3904-345X"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenye Zhao","raw_affiliation_strings":["Computer Science University of Illinois Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science University of Illinois Chicago","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089085275","display_name":"Cornelia Caragea","orcid":"https://orcid.org/0000-0002-5664-2163"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cornelia Caragea","raw_affiliation_strings":["Computer Science University of Illinois Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science University of Illinois Chicago","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39422238"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"29900","last_page":"29919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9864000082015991,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9864000082015991,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9818000197410583,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9797999858856201,"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/shot","display_name":"Shot (pellet)","score":0.7145455479621887},{"id":"https://openalex.org/keywords/zero","display_name":"Zero (linguistics)","score":0.6644982099533081},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5496066212654114},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4847133159637451},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40097978711128235},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.16541555523872375},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.07390269637107849}],"concepts":[{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.7145455479621887},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.6644982099533081},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5496066212654114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4847133159637451},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40097978711128235},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.16541555523872375},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.07390269637107849},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1446","pdf_url":"https://aclanthology.org/2025.acl-long.1446.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1446","pdf_url":"https://aclanthology.org/2025.acl-long.1446.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889691.pdf","grobid_xml":"https://content.openalex.org/works/W4412889691.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W3093517588"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Zero-shot":[0],"stance":[1],"detection":[2],"(ZSSD)":[3],"aims":[4],"to":[5,141],"determine":[6],"whether":[7],"the":[8,132,139],"author":[9],"of":[10,79,100,112,120,134],"a":[11,20,33,73,96,109],"text":[12],"is":[13,23],"in":[14,44,85],"support,":[15],"against,":[16],"or":[17],"neutral":[18],"toward":[19],"target":[21],"that":[22,46,127],"unseen":[24],"during":[25],"training.In":[26],"this":[27,68,129,143],"paper,":[28],"we":[29,70,94,122,137],"investigate":[30,147],"ZSSD":[31,65,76,102,145],"within":[32,60],"bilingual":[34,64,75,101],"framework":[35],"and":[36,41,57,62,88,156,164],"compare":[37],"it":[38],"with":[39,108,115],"cross-lingual":[40],"monolingual":[42],"scenarios,":[43],"settings":[45],"have":[47],"not":[48],"previously":[49],"been":[50],"explored.Our":[51],"study":[52],"focuses":[53],"on":[54,105],"both":[55,86],"noun-phrase":[56],"claim":[58,106],"targets":[59,107],"indomain":[61],"out-of-domain":[63],"scenarios.To":[66],"support":[67],"research,":[69],"assemble":[71],"Bi-STANCE,":[72,121],"comprehensive":[74],"dataset":[77,126,163],"consisting":[78],"over":[80],"100,000":[81],"annotated":[82],"text-target":[83],"pairs":[84],"Chinese":[87],"English,":[89],"sourced":[90],"from":[91],"existing":[92],"datasets.Additionally,":[93],"examine":[95],"more":[97],"challenging":[98,130],"aspect":[99],"by":[103],"focusing":[104],"low":[110],"occurrence":[111],"shared":[113],"words":[114],"their":[116],"corresponding":[117],"texts.As":[118],"part":[119],"created":[123],"an":[124],"extended":[125],"emphasizes":[128],"scenario.To":[131],"best":[133],"our":[135,162],"knowledge,":[136],"are":[138],"first":[140],"explore":[142],"difficult":[144],"setting.We":[146],"these":[148],"tasks":[149],"using":[150],"state-of-the-art":[151],"pre-trained":[152],"language":[153,158],"models":[154,159],"(PLMs)":[155],"large":[157],"(LLMs).We":[160],"release":[161],"code":[165],"at":[166],"https://github.com/chenyez/BiSTANCE.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
