{"id":"https://openalex.org/W4385477847","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192002","title":"Prompt-Learning for Cross-Lingual Relation Extraction","display_name":"Prompt-Learning for Cross-Lingual Relation Extraction","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385477847","doi":"https://doi.org/10.1109/ijcnn54540.2023.10192002"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10192002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10192002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5031568103","display_name":"Chiaming Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chiaming Hsu","raw_affiliation_strings":["JD Explore Academy","Wuhan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Explore Academy","institution_ids":[]},{"raw_affiliation_string":"Wuhan University","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007802059","display_name":"Changtong Zan","orcid":"https://orcid.org/0000-0002-5467-0937"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changtong Zan","raw_affiliation_strings":["JD Explore Academy","China University of Petroleum (East China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Explore Academy","institution_ids":[]},{"raw_affiliation_string":"China University of Petroleum (East China)","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456723","display_name":"Liang Ding","orcid":"https://orcid.org/0000-0001-8976-2084"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Ding","raw_affiliation_strings":["JD Explore Academy at JD.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD Explore Academy at JD.com","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088191810","display_name":"Longyue Wang","orcid":"https://orcid.org/0000-0002-9062-6183"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longyue Wang","raw_affiliation_strings":["Tencent AI Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100453810","display_name":"Xiaoting Wang","orcid":"https://orcid.org/0000-0001-6503-9158"},"institutions":[{"id":"https://openalex.org/I4210103986","display_name":"Jingdong (China)","ror":"https://ror.org/01dkjkq64","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103986"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoting Wang","raw_affiliation_strings":["JD.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"JD.com","institution_ids":["https://openalex.org/I4210103986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100444156","display_name":"Weifeng Liu","orcid":"https://orcid.org/0000-0002-5388-9080"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifeng Liu","raw_affiliation_strings":["China University of Petroleum (East China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Petroleum (East China)","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110219817","display_name":"Fu Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fu Lin","raw_affiliation_strings":["Wuhan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069789783","display_name":"Wenbin Hu","orcid":"https://orcid.org/0000-0002-8194-9941"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbin Hu","raw_affiliation_strings":["Wuhan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9835000038146973,"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":0.6880432367324829},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.6826887726783752},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5102846622467041},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5026671886444092},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5010757446289062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4884461462497711},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4447649419307709},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4146277904510498},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35549628734588623},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.27645373344421387},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19061321020126343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12728053331375122}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880432367324829},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.6826887726783752},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5102846622467041},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5026671886444092},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5010757446289062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4884461462497711},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4447649419307709},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4146277904510498},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35549628734588623},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.27645373344421387},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19061321020126343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12728053331375122},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10192002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn54540.2023.10192002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":89,"referenced_works":["https://openalex.org/W2407338347","https://openalex.org/W2567181374","https://openalex.org/W2794365787","https://openalex.org/W2807338375","https://openalex.org/W2896457183","https://openalex.org/W2910453440","https://openalex.org/W2955116803","https://openalex.org/W2962881743","https://openalex.org/W2963925437","https://openalex.org/W2964022985","https://openalex.org/W2964167098","https://openalex.org/W2970765893","https://openalex.org/W2971036674","https://openalex.org/W2971145411","https://openalex.org/W3001434439","https://openalex.org/W3034998639","https://openalex.org/W3034999214","https://openalex.org/W3080122044","https://openalex.org/W3091998909","https://openalex.org/W3098267758","https://openalex.org/W3099757670","https://openalex.org/W3102086967","https://openalex.org/W3102970018","https://openalex.org/W3113395007","https://openalex.org/W3113715281","https://openalex.org/W3113838476","https://openalex.org/W3153042221","https://openalex.org/W3153427360","https://openalex.org/W3160638507","https://openalex.org/W3161890805","https://openalex.org/W3172335055","https://openalex.org/W3173367141","https://openalex.org/W3173777717","https://openalex.org/W3174770825","https://openalex.org/W3175225269","https://openalex.org/W3185341429","https://openalex.org/W3188542058","https://openalex.org/W3196642073","https://openalex.org/W3214581470","https://openalex.org/W4224248112","https://openalex.org/W4224254875","https://openalex.org/W4224884173","https://openalex.org/W4224951189","https://openalex.org/W4285258432","https://openalex.org/W4287867774","https://openalex.org/W4292779060","https://openalex.org/W4292948773","https://openalex.org/W4296142184","https://openalex.org/W4296761723","https://openalex.org/W4310825537","https://openalex.org/W4321472103","https://openalex.org/W4321524280","https://openalex.org/W4322706667","https://openalex.org/W4324134461","https://openalex.org/W4360584537","https://openalex.org/W4365801687","https://openalex.org/W4385245566","https://openalex.org/W4385573252","https://openalex.org/W4389891246","https://openalex.org/W6714112401","https://openalex.org/W6739901393","https://openalex.org/W6749740488","https://openalex.org/W6752056141","https://openalex.org/W6755207826","https://openalex.org/W6769311223","https://openalex.org/W6773357711","https://openalex.org/W6773642575","https://openalex.org/W6778883912","https://openalex.org/W6784512941","https://openalex.org/W6787294940","https://openalex.org/W6788031712","https://openalex.org/W6790003725","https://openalex.org/W6794087632","https://openalex.org/W6798057236","https://openalex.org/W6799271272","https://openalex.org/W6800480908","https://openalex.org/W6810299469","https://openalex.org/W6810520126","https://openalex.org/W6810604754","https://openalex.org/W6810849777","https://openalex.org/W6838859767","https://openalex.org/W6841870936","https://openalex.org/W6842972219","https://openalex.org/W6843226161","https://openalex.org/W6845812702","https://openalex.org/W6847020704","https://openalex.org/W6850143541","https://openalex.org/W6850242479","https://openalex.org/W6850724024"],"related_works":["https://openalex.org/W2976808399","https://openalex.org/W2609844752","https://openalex.org/W2805262146","https://openalex.org/W2981341912","https://openalex.org/W4285246823","https://openalex.org/W4226278302","https://openalex.org/W4385734297","https://openalex.org/W2547211086","https://openalex.org/W4221160509","https://openalex.org/W3114142812"],"abstract_inverted_index":{"Relation":[0,36],"Extraction":[1,37],"(RE)":[2],"is":[3,28,39,61],"a":[4,17,81,168],"crucial":[5],"task":[6],"in":[7,31,44,150],"Information":[8],"Extraction,":[9],"which":[10,46],"entails":[11],"predicting":[12],"relationships":[13],"between":[14],"entities":[15],"within":[16],"given":[18],"sentence.":[19],"However,":[20],"extending":[21],"pre-trained":[22],"RE":[23],"models":[24],"to":[25,56,73,89],"other":[26,144,195],"languages":[27],"challenging,":[29],"particularly":[30],"real-world":[32],"scenarios":[33],"where":[34],"Cross-Lingual":[35],"(XRE)":[38],"required.":[40],"Despite":[41],"recent":[42],"advancements":[43],"Prompt-Learning,":[45],"involves":[47],"transferring":[48],"knowledge":[49],"from":[50,178],"Multilingual":[51],"Pre-trained":[52],"Language":[53],"Models":[54],"(PLMs)":[55],"diverse":[57],"downstream":[58],"tasks,":[59],"there":[60],"limited":[62],"research":[63],"on":[64,86,114,124,160,184],"the":[65,125,155,189],"effective":[66],"use":[67],"of":[68,157,191],"multilingual":[69,116,141],"PLMs":[70,142],"with":[71],"prompts":[72],"improve":[74],"XRE.":[75,151],"In":[76],"this":[77],"paper,":[78],"we":[79,96,164],"present":[80],"novel":[82],"XRE":[83,170,186],"algorithm":[84,136],"based":[85],"Prompt-Tuning,":[87],"referred":[88],"as":[90],"Prompt-Xre.":[91],"To":[92,152],"evaluate":[93],"its":[94],"effectiveness,":[95],"design":[97],"and":[98,106,109,143,166,200],"implement":[99],"several":[100],"prompt":[101],"templates,":[102],"including":[103],"hard,":[104],"soft,":[105],"hybrid":[107],"prompts,":[108],"empirically":[110],"test":[111],"their":[112],"performance":[113,149],"competitive":[115,196],"PLMs,":[117],"specifically":[118],"mBART.":[119],"Our":[120],"extensive":[121],"experiments,":[122],"conducted":[123],"low-resource":[126],"ACE05":[127],"benchmark":[128],"across":[129],"multiple":[130],"languages,":[131],"demonstrate":[132],"that":[133],"our":[134,158,192],"Prompt-Xre":[135],"significantly":[137],"outperforms":[138],"both":[139],"vanilla":[140],"existing":[145],"models,":[146],"achieving":[147],"state-of-the-art":[148],"further":[153],"show":[154,188],"generalization":[156],"Prompt-XRE":[159,193],"larger":[161],"data":[162],"scales,":[163],"construct":[165],"release":[167],"new":[169],"dataset-WMTI7-EnZh":[171],"XRE,":[172],"containing":[173],"0.9M":[174],"English-Chinese":[175],"pairs":[176],"extracted":[177],"WMT":[179],"2017":[180],"parallel":[181],"corpus.":[182],"Experiments":[183],"WMTI7-EnZh":[185],"also":[187],"effectiveness":[190],"against":[194],"baselines.":[197],"The":[198],"code":[199],"newly":[201],"constructed":[202],"dataset":[203],"are":[204],"freely":[205],"available":[206],"at":[207],"httus://2ithub.com/HSU-CHIA-MING/Promut-XRE.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
