{"id":"https://openalex.org/W7166888353","doi":"https://doi.org/10.18653/v1/2026.semeval-1.446","title":"SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models","display_name":"SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166888353","doi":"https://doi.org/10.18653/v1/2026.semeval-1.446"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.semeval-1.446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.semeval-1.446","pdf_url":"https://aclanthology.org/2026.semeval-1.446.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":"Proceedings of the 20th International Workshop on Semantic Evaluation (2026)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.semeval-1.446.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139775434","display_name":"Pengfei Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pengfei Cao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088113281","display_name":"Mingxuan Yang","orcid":"https://orcid.org/0000-0002-7772-3835"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingxuan Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139717762","display_name":"Yubo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yubo Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139743340","display_name":"Chenlong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenlong Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139838161","display_name":"Mingxuan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingxuan Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139757024","display_name":"Kang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139735441","display_name":"Jun Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jun Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87663581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3659","last_page":"3672"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.33320000767707825,"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":0.33320000767707825,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.12439999729394913,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.10890000313520432,"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/event","display_name":"Event (particle physics)","score":0.6018999814987183},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5584999918937683},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45509999990463257},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.2937000095844269},{"id":"https://openalex.org/keywords/event-data","display_name":"Event data","score":0.29260000586509705},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2856000065803528}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6758000254631042},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6018999814987183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569100022315979},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5584999918937683},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5465999841690063},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3059999942779541},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.semeval-1.446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.semeval-1.446","pdf_url":"https://aclanthology.org/2026.semeval-1.446.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":"Proceedings of the 20th International Workshop on Semantic Evaluation (2026)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.semeval-1.446","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.semeval-1.446","pdf_url":"https://aclanthology.org/2026.semeval-1.446.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":"Proceedings of the 20th International Workshop on Semantic Evaluation (2026)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.625408947467804,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[{"id":"https://openalex.org/G2070992019","display_name":null,"funder_award_id":"62406321","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5208670511","display_name":null,"funder_award_id":"U24A20335","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166888353.pdf","grobid_xml":"https://content.openalex.org/works/W7166888353.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"why":[1],"real-world":[2,66,111],"events":[3,112],"occur":[4],"is":[5],"important":[6],"for":[7,107,116],"both":[8],"natural":[9],"language":[10],"processing":[11],"and":[12,75,86,100,113,122],"practical":[13],"decision-making,":[14],"yet":[15],"direct-cause":[16],"inference":[17],"remains":[18],"underexplored":[19],"in":[20],"evidencerich":[21],"settings.To":[22],"address":[23],"this":[24],"gap,":[25],"we":[26],"organized":[27],"SemEval-2026":[28],"Task":[29],"12:":[30],"Abductive":[31],"Event":[32],"Reasoning":[33],"(AER).":[34],"1":[35],"The":[36],"task":[37,82,93],"asks":[38],"systems":[39],"to":[40],"identify":[41],"the":[42,92],"most":[43],"plausible":[44],"direct":[45],"cause":[46],"of":[47,65],"a":[48,104],"target":[49],"event":[50],"from":[51],"supporting":[52],"evidence.We":[53],"formulate":[54],"AER":[55],"as":[56],"an":[57],"evidence-grounded":[58],"multiplechoice":[59],"benchmark":[60,106],"that":[61],"captures":[62],"key":[63],"challenges":[64,115],"causal":[67,120],"reasoning,":[68],"including":[69],"distributed":[70],"evidence,":[71],"indirect":[72],"background":[73],"factors,":[74],"semantically":[76],"related":[77],"but":[78],"non-causal":[79],"distractors.The":[80],"shared":[81],"attracted":[83],"122":[84],"participants":[85],"received":[87],"518":[88],"submissions.This":[89],"paper":[90],"presents":[91],"formulation,":[94],"dataset":[95],"construction":[96],"pipeline,":[97],"evaluation":[98],"setup,":[99],"system":[101],"results.AER":[102],"provides":[103],"focused":[105],"abductive":[108],"reasoning":[109,121],"over":[110],"highlights":[114],"future":[117],"work":[118],"on":[119],"multi-document":[123],"understanding.":[124]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
