{"id":"https://openalex.org/W7166802834","doi":"https://doi.org/10.18653/v1/2026.eeuca-1.5","title":"When Tasks Share Structure: A Comparative Study of Training Strategies for Generative Event Extraction","display_name":"When Tasks Share Structure: A Comparative Study of Training Strategies for Generative Event Extraction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166802834","doi":"https://doi.org/10.18653/v1/2026.eeuca-1.5"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.eeuca-1.5","doi":"https://doi.org/10.18653/v1/2026.eeuca-1.5","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eeuca-1.5","pdf_url":"https://aclanthology.org/2026.eeuca-1.5.pdf","source":{"id":"https://openalex.org/S7407120963","display_name":"Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://aclanthology.org/2026.eeuca-1.5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126909543","display_name":"Rishi Ravikumar","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rishi Ravikumar","raw_affiliation_strings":["Department of Computer Science , University of Manchester , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , University of Manchester , United Kingdom","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050960711","display_name":"Riza Batista-Navarro","orcid":"https://orcid.org/0000-0001-6693-7531","observed_orcids":["https://orcid.org/0000-0001-6693-7531"]},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Riza Batista-Navarro","raw_affiliation_strings":["Department of Computer Science , University of Manchester , United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science , University of Manchester , United Kingdom","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43768356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"38","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9926999807357788,"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":0.9926999807357788,"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":0.0044999998062849045,"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":0.0008999999845400453,"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/event-extraction","display_name":"event extraction","score":0.8820000290870667},{"id":"https://openalex.org/keywords/event-detection","display_name":"event detection","score":0.7789999842643738},{"id":"https://openalex.org/keywords/event-argument-extraction","display_name":"event argument extraction","score":0.9679999947547913},{"id":"https://openalex.org/keywords/training-strategies","display_name":"training strategies","score":0.5860000252723694},{"id":"https://openalex.org/keywords/weight-allocation","display_name":"weight allocation","score":0.6439999938011169},{"id":"https://openalex.org/keywords/joint-training","display_name":"joint training","score":0.6460000276565552},{"id":"https://openalex.org/keywords/task-decomposition","display_name":"task decomposition","score":0.38199999928474426},{"id":"https://openalex.org/keywords/information-extraction","display_name":"information extraction","score":0.7210000157356262},{"id":"https://openalex.org/keywords/instruction-tuned-language-models","display_name":"instruction-tuned language models","score":0.6779999732971191},{"id":"https://openalex.org/keywords/ace2005","display_name":"ACE2005","score":0.9150000214576721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6539000272750854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5982999801635742},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5139999985694885},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32910001277923584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3257000148296356},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25859999656677246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.eeuca-1.5","doi":"https://doi.org/10.18653/v1/2026.eeuca-1.5","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eeuca-1.5","pdf_url":"https://aclanthology.org/2026.eeuca-1.5.pdf","source":{"id":"https://openalex.org/S7407120963","display_name":"Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.eeuca-1.5","doi":"https://doi.org/10.18653/v1/2026.eeuca-1.5","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eeuca-1.5","pdf_url":"https://aclanthology.org/2026.eeuca-1.5.pdf","source":{"id":"https://openalex.org/S7407120963","display_name":"Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"conference"},"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 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166802834.pdf","grobid_xml":"https://content.openalex.org/works/W7166802834.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7078600597","https://openalex.org/W7214342592","https://openalex.org/W7140161706","https://openalex.org/W2952437275","https://openalex.org/W4416035227","https://openalex.org/W7015644565","https://openalex.org/W4416034606","https://openalex.org/W4386566687","https://openalex.org/W4402684024","https://openalex.org/W3197503334"],"abstract_inverted_index":{"Event":[0],"extraction":[1,81,102,106],"requires":[2],"performing":[3],"two":[4],"interdependent":[5,111],"subtasks:":[6],"event":[7,10,101],"detection":[8],"and":[9,18,50,59,63,77,83,92],"argument":[11],"extraction.While":[12],"prior":[13],"work":[14],"has":[15,74],"explored":[16],"pipelined":[17],"joint":[19],"training":[20,29,45,72],"approaches,":[21],"the":[22],"question":[23],"of":[24],"how":[25],"best":[26],"to":[27,103],"coordinate":[28],"across":[30,65,90],"these":[31,96],"subtasks":[32],"in":[33],"generative":[34],"LLM-based":[35],"systems":[36],"remains":[37],"open.We":[38],"present":[39],"a":[40,75,85],"systematic":[41],"study":[42],"comparing":[43],"three":[44],"paradigms:":[46],"disjoint,":[47],"fully":[48],"shared":[49],"hybrid":[51],"weight":[52],"allocation,":[53],"instantiated":[54],"as":[55],"eight":[56],"concrete":[57],"strategies":[58],"evaluated":[60],"on":[61,80],"ACE2005":[62],"RichERE":[64],"multiple":[66],"instruction-tuned":[67],"LLMs.Our":[68],"findings":[69,97],"show":[70],"that":[71,84,95,108],"strategy":[73,88],"consistent":[76],"meaningful":[78],"effect":[79],"accuracy,":[82],"clear":[86],"best-performing":[87],"emerges":[89],"models":[91],"benchmarks.We":[93],"believe":[94],"could":[98],"extend":[99],"beyond":[100],"other":[104],"information":[105],"tasks":[107],"decompose":[109],"into":[110],"subtasks.":[112]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-07-02T00:00:00"}
