{"id":"https://openalex.org/W7105698664","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227821","title":"Enhancing Script Event Prediction Through Contrastive Fine-Tuning on Semantic Similarity","display_name":"Enhancing Script Event Prediction Through Contrastive Fine-Tuning on Semantic Similarity","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105698664","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227821"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Jialiang Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialiang Wu","raw_affiliation_strings":["School of Computer Science and Technology, Hainan University,Haikou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hainan University,Haikou,China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chunyang Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyang Ye","raw_affiliation_strings":["School of Computer Science and Technology, Hainan University,Haikou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hainan University,Haikou,China","institution_ids":["https://openalex.org/I20942203"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yongji Sui","orcid":null},"institutions":[{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongji Sui","raw_affiliation_strings":["School of Computer Science and Technology, Hainan University,Haikou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hainan University,Haikou,China","institution_ids":["https://openalex.org/I20942203"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73793926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8884999752044678,"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":0.8884999752044678,"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":0.03440000116825104,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.021700000390410423,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6248999834060669},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6107000112533569},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5773000121116638},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5328999757766724},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5152000188827515},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49639999866485596},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.49390000104904175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7919999957084656},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7197999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6866000294685364},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6248999834060669},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6107000112533569},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5773000121116638},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5328999757766724},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5152000188827515},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.49390000104904175},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C67277372","wikidata":"https://www.wikidata.org/wiki/Q7449085","display_name":"Semantic role labeling","level":3,"score":0.3206000030040741},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.259799987077713},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2257051837","https://openalex.org/W2758362814","https://openalex.org/W2758815496","https://openalex.org/W2904617336","https://openalex.org/W2949875129","https://openalex.org/W2963101081","https://openalex.org/W2964080504","https://openalex.org/W3012590175","https://openalex.org/W3034999214","https://openalex.org/W3102333984","https://openalex.org/W3118202953","https://openalex.org/W3156636935","https://openalex.org/W3173937547","https://openalex.org/W3206836752","https://openalex.org/W3209562714","https://openalex.org/W4221143296","https://openalex.org/W4281476939","https://openalex.org/W4293547730","https://openalex.org/W4382202620"],"related_works":[],"abstract_inverted_index":{"Script":[0],"event":[1,29,111],"prediction":[2,112],"involves":[3],"inferring":[4],"subsequent":[5],"events":[6],"in":[7],"a":[8,63,102],"sequence":[9],"from":[10],"incomplete":[11],"scripts.":[12],"Despite":[13],"the":[14,93,124,144],"success":[15],"of":[16,71,146],"models":[17,44],"pretrained":[18],"with":[19,101],"external":[20,46,96,116],"knowledge,":[21],"challenges":[22],"persist.":[23],"Discourse-based":[24],"methods":[25],"capture":[26],"only":[27],"explicit":[28],"relations,":[30],"missing":[31],"implicit":[32],"ones,":[33],"and":[34,40,81],"constructing":[35],"knowledge":[36,47,117],"bases":[37],"is":[38,99],"costly":[39],"time-consuming.":[41],"In":[42],"contrast,":[43],"without":[45,113,138],"often":[48],"focus":[49],"on":[50,68,107,115,123],"token-level":[51],"semantics,":[52],"overlooking":[53],"critical":[54],"event-level":[55,86],"information.":[56],"To":[57],"address":[58],"these":[59],"limitations,":[60],"we":[61],"propose":[62],"contrastive":[64,104],"fine-tuning":[65,141],"model":[66,120],"based":[67,106],"semantic":[69,108],"similarity":[70,78],"general":[72],"sentence":[73],"embeddings.":[74],"Our":[75],"approach":[76,142],"calculates":[77],"between":[79],"generated":[80],"candidate":[82],"sentences":[83],"using":[84],"an":[85],"blank":[87],"infilling":[88],"strategy":[89],"during":[90],"pre-training,":[91,139],"eliminating":[92],"need":[94],"for":[95],"knowledge.":[97],"Fine-tuning":[98],"performed":[100],"custom":[103],"loss":[105],"similarity,":[109],"enabling":[110],"reliance":[114],"or":[118],"added":[119],"complexity.":[121],"Experiments":[122],"Multi-Choice":[125],"Narrative":[126],"Cloze":[127],"(MCNC)":[128],"task":[129],"demonstrate":[130],"that":[131],"our":[132,140],"method":[133],"outperforms":[134],"state-of-the-art":[135],"baselines.":[136],"Even":[137],"matches":[143],"performance":[145],"competitive":[147],"models.":[148]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-14T00:00:00"}
