{"id":"https://openalex.org/W4391550294","doi":"https://doi.org/10.1145/3637494.3637512","title":"Multilingual Entity and Relation Extraction from Unified to Language-specific Training","display_name":"Multilingual Entity and Relation Extraction from Unified to Language-specific Training","publication_year":2023,"publication_date":"2023-11-17","ids":{"openalex":"https://openalex.org/W4391550294","doi":"https://doi.org/10.1145/3637494.3637512"},"language":"en","primary_location":{"id":"doi:10.1145/3637494.3637512","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637494.3637512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Electronics, Computers and Communication Technology","raw_type":"proceedings-article"},"type":"article","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/A5081677641","display_name":"Zixiang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zixiang Wang","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343820","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0003-1983-012X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016178437","display_name":"Tongliang Li","orcid":"https://orcid.org/0000-0002-2488-2787"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongliang Li","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077248623","display_name":"Linzheng Chai","orcid":"https://orcid.org/0009-0001-2129-3207"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linzheng Chai","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032858379","display_name":"Jiaheng Liu","orcid":"https://orcid.org/0000-0002-5183-8538"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaheng Liu","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101956347","display_name":"Ying Mo","orcid":"https://orcid.org/0000-0002-5149-2207"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Mo","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101946453","display_name":"Jiaqi Bai","orcid":"https://orcid.org/0000-0001-8312-6992"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Bai","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036786337","display_name":"Zhoujun Li","orcid":"https://orcid.org/0000-0002-9603-9713"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhoujun Li","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5081677641"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.3479,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68505838,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"98","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9966999888420105,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9966999888420105,"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/T10028","display_name":"Topic Modeling","score":0.9794999957084656,"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":0.8431599736213684},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7705997228622437},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6996848583221436},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6581516861915588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5837482213973999},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4578808844089508},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.38569164276123047},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.3481229543685913},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33356955647468567},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.12696900963783264}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8431599736213684},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7705997228622437},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6996848583221436},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6581516861915588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5837482213973999},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4578808844089508},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.38569164276123047},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3481229543685913},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33356955647468567},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.12696900963783264},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637494.3637512","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637494.3637512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Electronics, Computers and Communication Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2899015110","https://openalex.org/W2919290281","https://openalex.org/W2962881743","https://openalex.org/W2963247703","https://openalex.org/W2996822578","https://openalex.org/W3017454464","https://openalex.org/W3034744126","https://openalex.org/W3035390927","https://openalex.org/W3096966601","https://openalex.org/W3101577648","https://openalex.org/W3102859667","https://openalex.org/W3105005398","https://openalex.org/W3106321930","https://openalex.org/W3116645343","https://openalex.org/W3156772763","https://openalex.org/W3187731984","https://openalex.org/W4285600847","https://openalex.org/W4285602051","https://openalex.org/W4287891008"],"related_works":["https://openalex.org/W2976808399","https://openalex.org/W2609844752","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","https://openalex.org/W4380551175"],"abstract_inverted_index":{"Entity":[0,105],"and":[1,25,35,99,106,201,223],"relation":[2,26,182],"extraction":[3,27,222],"is":[4,41,53,215,225],"a":[5,47,94,100,127,153],"key":[6],"task":[7,28],"in":[8,46,77,122],"information":[9],"extraction,":[10,175],"where":[11],"the":[12,32,81,133,140,164,172,176,181,188,199,239],"output":[13],"can":[14],"be":[15],"used":[16,55,185],"for":[17,23],"downstream":[18],"NLP":[19],"tasks.":[20],"Existing":[21],"approaches":[22],"entity":[24],"mainly":[29],"focus":[30],"on":[31,219],"English":[33],"corpora":[34],"ignore":[36],"other":[37,68,230],"languages.":[38,71,88,116],"Thus,":[39],"it":[40],"critical":[42],"to":[43,56,67,111,125,148,162,170,186,211,227,229],"improve":[44],"performance":[45,59],"multilingual":[48,51,78,96,202],"setting.":[49],"Meanwhile,":[50],"training":[52,97],"usually":[54,75],"boost":[57],"cross-lingual":[58],"by":[60,138],"transferring":[61],"knowledge":[62],"from":[63],"languages":[64,124],"(e.g.,":[65,69],"high-resource)":[66],"low-resource)":[70],"However,":[72],"language":[73,113,142],"interference":[74,114,150],"exists":[76],"tasks":[79],"as":[80],"model":[82,102],"parameters":[83,147],"are":[84,184],"shared":[85],"among":[86],"all":[87],"In":[89],"this":[90],"paper,":[91],"we":[92,118,145,206],"propose":[93],"two-stage":[95],"method":[98,196],"joint":[101],"called":[103],"Multilingual":[104],"Relation":[107],"Extraction":[108],"framework":[109],"(mERE)":[110],"mitigate":[112,149],"across":[115],"Specifically,":[117],"randomly":[119],"concatenate":[120],"sentences":[121],"different":[123],"train":[126],"Language-universal":[128],"Aggregator":[129],"(LA),":[130],"which":[131,157,236],"narrows":[132],"distance":[134],"of":[135,233,241],"embedding":[136],"representations":[137,178],"obtaining":[139],"unified":[141],"representation.":[143,167],"Then,":[144],"separate":[146],"via":[151],"tuning":[152],"Language-specific":[154],"Switcher":[155],"(LS),":[156],"includes":[158],"several":[159],"independent":[160],"submodules":[161],"refine":[163],"language-specific":[165],"feature":[166,183],"After":[168],"that,":[169],"enhance":[171],"relational":[173,220],"triple":[174,221],"sentence":[177],"concatenated":[179],"with":[180],"recognize":[187],"entities.":[189],"Extensive":[190],"experimental":[191],"results":[192],"show":[193,212],"that":[194,213],"our":[195,242],"outperforms":[197],"both":[198],"monolingual":[200],"baseline":[203],"methods.":[204],"Besides,":[205],"also":[207],"perform":[208],"detailed":[209],"analysis":[210],"mERE":[214,224],"lightweight":[216],"but":[217],"effective":[218],"easy":[226],"transfer":[228],"backbone":[231],"models":[232],"multi-field":[234],"tasks,":[235],"further":[237],"demonstrates":[238],"effectiveness":[240],"method.":[243]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
