{"id":"https://openalex.org/W4388405359","doi":"https://doi.org/10.1109/dsaa60987.2023.10302466","title":"Learning Representations through Contrastive Strategies for a more Robust Stance Detection","display_name":"Learning Representations through Contrastive Strategies for a more Robust Stance Detection","publication_year":2023,"publication_date":"2023-10-09","ids":{"openalex":"https://openalex.org/W4388405359","doi":"https://doi.org/10.1109/dsaa60987.2023.10302466"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa60987.2023.10302466","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa60987.2023.10302466","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092795211","display_name":"Udhaya Kumar Rajendran","orcid":null},"institutions":[{"id":"https://openalex.org/I72541430","display_name":"Lakehead University","ror":"https://ror.org/023p7mg82","country_code":"CA","type":"education","lineage":["https://openalex.org/I72541430"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Udhaya Kumar Rajendran","raw_affiliation_strings":["Lakehead University,Department of Computer Science,Canada","Department of Computer Science, Lakehead University, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lakehead University,Department of Computer Science,Canada","institution_ids":["https://openalex.org/I72541430"]},{"raw_affiliation_string":"Department of Computer Science, Lakehead University, Canada","institution_ids":["https://openalex.org/I72541430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051429607","display_name":"A. Ben Haj Khalifa","orcid":null},"institutions":[{"id":"https://openalex.org/I83259278","display_name":"Manouba University","ror":"https://ror.org/0503ejf32","country_code":"TN","type":"education","lineage":["https://openalex.org/I83259278"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Amir Ben Khalifa","raw_affiliation_strings":["Université de La Manouba,École Nationale des Sciences de l’Informatique,Tunisia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Université de La Manouba,École Nationale des Sciences de l’Informatique,Tunisia","institution_ids":["https://openalex.org/I83259278"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038712780","display_name":"Amine Trabelsi","orcid":"https://orcid.org/0000-0002-1852-5265"},"institutions":[{"id":"https://openalex.org/I135117807","display_name":"Universit\u00e9 de Sherbrooke","ror":"https://ror.org/00kybxq39","country_code":"CA","type":"education","lineage":["https://openalex.org/I135117807"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Amine Trabelsi","raw_affiliation_strings":["Université de Sherbrooke,Department of Computer Science,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Université de Sherbrooke,Department of Computer Science,Canada","institution_ids":["https://openalex.org/I135117807"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16185221,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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":0.9991999864578247,"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":0.998199999332428,"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/T13629","display_name":"Text Readability and Simplification","score":0.9977999925613403,"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":0.8370603919029236},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6593397855758667},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5806167721748352},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5429503917694092},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.522883415222168},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5085092782974243},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5043240785598755},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4497692883014679},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44962602853775024}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8370603919029236},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6593397855758667},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5806167721748352},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5429503917694092},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.522883415222168},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5085092782974243},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5043240785598755},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4497692883014679},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44962602853775024},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa60987.2023.10302466","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa60987.2023.10302466","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1507711477","https://openalex.org/W1975879668","https://openalex.org/W1981039744","https://openalex.org/W2460159515","https://openalex.org/W2741386817","https://openalex.org/W2750779823","https://openalex.org/W2768226620","https://openalex.org/W2788292930","https://openalex.org/W2798367316","https://openalex.org/W2896457183","https://openalex.org/W2946364534","https://openalex.org/W2978017171","https://openalex.org/W2984259019","https://openalex.org/W3005971640","https://openalex.org/W3027173706","https://openalex.org/W3092642435","https://openalex.org/W3098096010","https://openalex.org/W3115295967","https://openalex.org/W3140514123","https://openalex.org/W3148568192","https://openalex.org/W3173783447","https://openalex.org/W3194908112","https://openalex.org/W3197907569","https://openalex.org/W3199832406","https://openalex.org/W3199926081","https://openalex.org/W3201336341","https://openalex.org/W3203711169","https://openalex.org/W3209527742","https://openalex.org/W3211640232","https://openalex.org/W3213520368","https://openalex.org/W3213881810","https://openalex.org/W4205807230","https://openalex.org/W4205864287","https://openalex.org/W4224311791","https://openalex.org/W4230418757","https://openalex.org/W4239019441","https://openalex.org/W4284664815","https://openalex.org/W4285254085","https://openalex.org/W4292779060","https://openalex.org/W4385567522","https://openalex.org/W6681699098","https://openalex.org/W6763331066","https://openalex.org/W6767189354","https://openalex.org/W6768851824","https://openalex.org/W6779073920","https://openalex.org/W6784001418","https://openalex.org/W6794355302","https://openalex.org/W6796899183","https://openalex.org/W6800780714","https://openalex.org/W6801537427","https://openalex.org/W6804136108","https://openalex.org/W6810616966","https://openalex.org/W6839340644"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W2597655663","https://openalex.org/W4281476908"],"abstract_inverted_index":{"Stance":[0,27],"Detection":[1,28],"refers":[2],"to":[3,33,49,68,118,124,133,149,174],"the":[4,64,72,90,95,98,119,125,151,159],"process":[5],"of":[6,97,155],"determining":[7],"an":[8],"author\u2019s":[9],"position":[10],"towards":[11],"a":[12,18,81],"particular":[13],"issue":[14],"or":[15],"target":[16],"in":[17,38,71],"text.":[19],"Previous":[20],"research":[21],"suggests":[22],"that":[23,85,109],"existing":[24],"systems":[25],"for":[26],"are":[29,168],"not":[30],"resilient":[31],"enough":[32],"handle":[34],"variations":[35],"and":[36,61,102,107,114,138,158,171],"errors":[37],"input":[39],"sentences.":[40],"In":[41],"our":[42,77,110,163],"proposed":[43],"methodology,":[44],"we":[45,79],"utilize":[46],"Contrastive":[47],"Learning":[48],"learn":[50],"sentence":[51],"representations.":[52],"We":[53,93,142],"achieve":[54],"this":[55],"by":[56],"bringing":[57],"semantically":[58],"similar":[59],"sentences":[60],"those":[62,175],"implying":[63],"same":[65],"stance":[66,91,140,147],"closer":[67],"each":[69],"other":[70],"embedding":[73],"space.":[74],"To":[75],"compare":[76],"approach,":[78],"use":[80],"pretrained":[82],"transformer":[83],"model":[84],"is":[86,115,130],"directly":[87],"finetuned":[88],"with":[89,145,162,177],"datasets.":[92,141],"evaluate":[94],"resilience":[96],"models":[99,160],"using":[100],"char-level":[101],"word-level":[103],"adversarial":[104,121],"perturbation":[105],"attacks":[106],"show":[108],"approach":[111,129,164],"performs":[112],"better":[113,135],"more":[116],"robust":[117,170],"different":[120],"perturbations":[122],"introduced":[123],"test":[126],"data.":[127,179],"Our":[128],"also":[131],"shown":[132],"perform":[134,172],"on":[136,165],"small-sized":[137],"class-imbalanced":[139],"further":[143],"experiment":[144],"unlabeled":[146,166],"datasets":[148,167],"make":[150],"representation":[152],"learning":[153],"independent":[154],"domain-specific":[156],"labels,":[157],"trained":[161,176],"still":[169],"comparably":[173],"labeled":[178]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}