{"id":"https://openalex.org/W7126409500","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.38","title":"Bootstrap Your Own PLM: Boosting Semantic Features of PLMs for Unsuperivsed Contrastive Learning","display_name":"Bootstrap Your Own PLM: Boosting Semantic Features of PLMs for Unsuperivsed Contrastive Learning","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W7126409500","doi":"https://doi.org/10.18653/v1/2024.findings-eacl.38"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2024.findings-eacl.38","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.38","pdf_url":"https://aclanthology.org/2024.findings-eacl.38.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: EACL 2024","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2024.findings-eacl.38.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070216383","display_name":"Yoo Hyun jeong","orcid":null},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoo Hyun Jeong","raw_affiliation_strings":["Department of Artificial Intelligence , Hanyang University , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence , Hanyang University , South Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066689713","display_name":"Myeong Soo Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Myeong Soo Han","raw_affiliation_strings":["Department of Artificial Intelligence , Hanyang University , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence , Hanyang University , South Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124605687","display_name":"Dong-Kyu Chae","orcid":null},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dong-Kyu Chae","raw_affiliation_strings":["Department of Artificial Intelligence , Hanyang University , South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence , Hanyang University , South Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4575257"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.56972067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"560","last_page":"569"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.35199999809265137,"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":0.35199999809265137,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0828000009059906,"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":0.0640999972820282,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.666700005531311},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.35100001096725464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34470000863075256},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.30809998512268066},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.28780001401901245}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.666700005531311},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6592000126838684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5946999788284302},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.39559999108314514},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34360000491142273},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2024.findings-eacl.38","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.38","pdf_url":"https://aclanthology.org/2024.findings-eacl.38.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: EACL 2024","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2024.findings-eacl.38","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2024.findings-eacl.38","pdf_url":"https://aclanthology.org/2024.findings-eacl.38.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: EACL 2024","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G107372574","display_name":null,"funder_award_id":"2020-0-01373","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G44117325","display_name":null,"funder_award_id":"2018R1A5A7059549","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G5051733337","display_name":null,"funder_award_id":"2020-0-01373","funder_id":"https://openalex.org/F4320321142","funder_display_name":"Hanyang University"},{"id":"https://openalex.org/G5323172026","display_name":null,"funder_award_id":"2020-0-01373","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G7335664070","display_name":null,"funder_award_id":"2020-0-01373","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G8133777603","display_name":null,"funder_award_id":"2018R1A5A7059549","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8893589656","display_name":null,"funder_award_id":"No. 2018R1A5A7059549","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321142","display_name":"Hanyang University","ror":"https://ror.org/046865y68"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126409500.pdf","grobid_xml":"https://content.openalex.org/works/W7126409500.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,54,73,105,130],"investigate":[4],"the":[5,21,28,44,76,89,95,112,131,137],"possibility":[6],"of":[7,12,23,30,46,78,92,133,139],"exploiting":[8],"original":[9],"semantic":[10,108],"features":[11,113],"PLMs":[13,116],"(pre-trained":[14],"language":[15],"models)":[16],"during":[17],"contrastive":[18,47],"learning":[19],"in":[20],"context":[22,29],"SRL":[24],"(sentence":[25],"representation":[26,52],"learning).In":[27],"feature":[31,40],"modification,":[32],"we":[33],"identified":[34],"a":[35],"method":[36],"called":[37],"IFM":[38,62],"(implicit":[39],"modification),":[41],"which":[42,69,84,135],"reduces":[43],"tendency":[45],"models":[48],"for":[49,67],"VRL":[50,79],"(visual":[51],"learning)":[53],"rely":[55],"on":[56],"feature-suppressing":[57],"shortcut":[58],"solutions.We":[59],"observed":[60],"that":[61,97,111],"did":[63],"not":[64],"work":[65],"well":[66],"SRL,":[68],"may":[70,101],"be":[71,102,122],"due":[72],"differences":[74],"between":[75],"nature":[77,138],"and":[80,110,120],"SRL.We":[81],"propose":[82],"BYOP,":[83,134],"boosts":[85],"well-represented":[86],"features,":[87,109],"taking":[88],"opposite":[90],"idea":[91],"IFM,":[93],"under":[94],"assumption":[96],"SimCSE's":[98],"dropoutnoise-based":[99],"augmentation":[100],"too":[103],"simple":[104],"modify":[106],"high-level":[107],"learned":[114],"by":[115],"are":[117],"semantically":[118],"meaningful":[119],"should":[121],"boosted,":[123],"rather":[124],"than":[125],"removed.Extensive":[126],"experiments":[127],"lend":[128],"credence":[129],"logic":[132],"considers":[136],"SRL.Our":[140],"code":[141],"is":[142],"publicly":[143],"available":[144],"at":[145],"https://github.com/myngsooo/BYOP.":[146]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-02-02T00:00:00"}
