{"id":"https://openalex.org/W3034893848","doi":"https://doi.org/10.1145/3397271.3401173","title":"Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation","display_name":"Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3034893848","doi":"https://doi.org/10.1145/3397271.3401173","mag":"3034893848"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401173","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5059878190","display_name":"Jianming Zheng","orcid":"https://orcid.org/0000-0002-5594-256X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianming Zheng","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066699855","display_name":"Fei Cai","orcid":"https://orcid.org/0000-0002-5709-1682"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Cai","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026327666","display_name":"Honghui Chen","orcid":"https://orcid.org/0009-0003-5234-0592"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Honghui Chen","raw_affiliation_strings":["National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":2.2999,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.90827969,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"249","last_page":"258"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9958999752998352,"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/T11719","display_name":"Data Quality and Management","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8116063475608826},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7666454315185547},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.69024658203125},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6116610765457153},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4841245114803314},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.45326468348503113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44313088059425354},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41098690032958984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8116063475608826},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7666454315185547},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.69024658203125},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6116610765457153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4841245114803314},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.45326468348503113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44313088059425354},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41098690032958984},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3397271.3401173","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1566289585","https://openalex.org/W2008269560","https://openalex.org/W2151295812","https://openalex.org/W2158794898","https://openalex.org/W2251939518","https://openalex.org/W2740582239","https://openalex.org/W2758362814","https://openalex.org/W2788044935","https://openalex.org/W2798812533","https://openalex.org/W2904617336","https://openalex.org/W2910557093","https://openalex.org/W2949345771","https://openalex.org/W2949875129","https://openalex.org/W2950577311","https://openalex.org/W2962739339","https://openalex.org/W2963026768","https://openalex.org/W2964080504","https://openalex.org/W2970169050","https://openalex.org/W2974992637","https://openalex.org/W3106003309","https://openalex.org/W4293547730","https://openalex.org/W4302343710","https://openalex.org/W4310661400","https://openalex.org/W4320013820","https://openalex.org/W4320922361","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W2062195135","https://openalex.org/W4321636575","https://openalex.org/W1986418932","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W2156919374","https://openalex.org/W1483472507","https://openalex.org/W1984019423"],"abstract_inverted_index":{"Given":[0],"an":[1],"occurred":[2],"event,":[3,15],"human":[4],"can":[5,47,188,201],"easily":[6,88],"predict":[7],"the":[8,13,30,36,96,164,175,181,197,203],"next":[9],"event":[10,25,39,79,100,107,123],"or":[11],"reason":[12],"preceding":[14],"yet":[16],"which":[17,45,86,127],"is":[18,87,102],"difficult":[19],"for":[20,106,122,192,213],"machine":[21],"to":[22,34,67,71,149,161],"perform":[23],"such":[24],"reasoning.":[26],"Event":[27],"representation":[28,176,182],"bridges":[29],"connection":[31],"and":[32,60,136,155,177],"targets":[33],"model":[35,186],"process":[37],"of":[38,52,75,130,153,211],"reasoning":[40],"as":[41],"a":[42,49,68,82,91,114,131,137,146,158],"machine-readable":[43],"format,":[44],"then":[46],"support":[48],"wide":[50],"range":[51],"applications":[53],"in":[54,78,99,209],"information":[55,61],"retrieval,":[56],"e.g.,":[57],"question":[58],"answering":[59],"extraction.":[62],"Existing":[63],"work":[64],"mainly":[65,128],"resorts":[66],"joint":[69],"training":[70,76,154],"integrate":[72,150],"all":[73,151],"levels":[74,152],"loss":[77,84],"chains":[80,101],"by":[81],"simple":[83],"summation,":[85],"trapped":[89],"into":[90],"local":[92],"optimum.":[93],"In":[94,109,142],"addition,":[95],"scenario":[97,120],"knowledge":[98,121],"not":[103],"well":[104],"investigated":[105],"representation.":[108],"this":[110],"paper,":[111],"we":[112],"propose":[113],"unified":[115,132],"fine-tuning":[116,133,148],"architecture,":[117],"incorporated":[118],"with":[119],"representation,":[124],"i.e.,":[125,174],"UniFA-S,":[126],"consists":[129],"architecture":[134],"(UniFA)":[135],"scenario-level":[138,165],"variational":[139],"auto-encoder":[140],"(S-VAE).":[141],"detail,":[143],"UniFA":[144],"employs":[145],"multi-step":[147],"S-VAE":[156],"applies":[157],"stochastic":[159],"variable":[160],"implicitly":[162],"represent":[163],"knowledge.":[166],"We":[167],"evaluate":[168],"our":[169,184],"proposal":[170],"from":[171],"two":[172,193],"aspects,":[173],"inference":[178,198,215],"abilities.":[179],"For":[180,196],"ability,":[183,199],"ensemble":[185],"UniFA-S":[187,200],"beat":[189],"state-of-the-art":[190],"baselines":[191],"similarity":[194],"tasks.":[195,216],"outperform":[202],"best":[204],"baseline,":[205],"achieving":[206],"4.1%-8.2%":[207],"improvements":[208],"terms":[210],"accuracy":[212],"various":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
