{"id":"https://openalex.org/W4379378423","doi":"https://doi.org/10.21428/594757db.8702fa2f","title":"Few shot learning approaches to essay scoring","display_name":"Few shot learning approaches to essay scoring","publication_year":2023,"publication_date":"2023-06-05","ids":{"openalex":"https://openalex.org/W4379378423","doi":"https://doi.org/10.21428/594757db.8702fa2f"},"language":"en","primary_location":{"id":"doi:10.21428/594757db.8702fa2f","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.8702fa2f","pdf_url":"https://caiac.pubpub.org/pub/gdf5n6gs/download/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 Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://caiac.pubpub.org/pub/gdf5n6gs/download/pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044054976","display_name":"Robert K. Helmeczi","orcid":null},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Robert K. Helmeczi","raw_affiliation_strings":["Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037002753","display_name":"Savas Y\u0131ld\u0131r\u0131m","orcid":null},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Savas Y\u0131ld\u0131r\u0131m","raw_affiliation_strings":["Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070091996","display_name":"M\u00fccahit \u00c7evik","orcid":"https://orcid.org/0000-0003-4020-6305"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Mucahit Cevik","raw_affiliation_strings":["Toronto Metropolitan University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063090617","display_name":"Sojin Lee","orcid":"https://orcid.org/0000-0002-9054-572X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sojin Lee","raw_affiliation_strings":["Blees AI, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Blees AI, Toronto, Canada","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070091996"],"corresponding_institution_ids":["https://openalex.org/I530967"],"apc_list":null,"apc_paid":null,"fwci":0.5237,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70045317,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9961000084877014,"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":0.9961000084877014,"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/T14025","display_name":"Educational Technology and Assessment","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9717000126838684,"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":0.7964977025985718},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.7197651863098145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6975424885749817},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6229186654090881},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.5536952614784241},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5151028037071228},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.44697320461273193}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964977025985718},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.7197651863098145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6975424885749817},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6229186654090881},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.5536952614784241},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5151028037071228},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.44697320461273193},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21428/594757db.8702fa2f","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.8702fa2f","pdf_url":"https://caiac.pubpub.org/pub/gdf5n6gs/download/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 Canadian Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21428/594757db.8702fa2f","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.8702fa2f","pdf_url":"https://caiac.pubpub.org/pub/gdf5n6gs/download/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 Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8700000047683716,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320331257","display_name":"Alliance de recherche num\u00e9rique du Canada","ror":"https://ror.org/010r6td27"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379378423.pdf"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W2567547739","https://openalex.org/W2616957565","https://openalex.org/W2740592764","https://openalex.org/W2963211188","https://openalex.org/W2963341956","https://openalex.org/W2964413085","https://openalex.org/W3030163527","https://openalex.org/W3034730645","https://openalex.org/W3037207300","https://openalex.org/W3037461693","https://openalex.org/W3096580779","https://openalex.org/W3119438769","https://openalex.org/W3134748792","https://openalex.org/W3153427360","https://openalex.org/W3168867926","https://openalex.org/W3172642864","https://openalex.org/W3174770825","https://openalex.org/W3185341429","https://openalex.org/W3201464860","https://openalex.org/W3205068155","https://openalex.org/W3205949070","https://openalex.org/W4205991051","https://openalex.org/W4206214875","https://openalex.org/W4280510965","https://openalex.org/W4280534475","https://openalex.org/W4286953959","https://openalex.org/W4287323244","https://openalex.org/W4292779060","https://openalex.org/W4296959557","https://openalex.org/W4308610003","https://openalex.org/W6793601707"],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W3044321615","https://openalex.org/W4294892107","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2357748469","https://openalex.org/W2392917037","https://openalex.org/W2046675961"],"abstract_inverted_index":{"Automated":[0],"essay":[1,63],"scoring":[2],"(AES)":[3],"involves":[4,52],"using":[5],"computer":[6],"technology":[7],"to":[8,34,41,61,82,142,154],"grade":[9],"written":[10],"assessments":[11],"and":[12,37,107,114,134],"assigning":[13],"a":[14,47,54,69,98,108],"score":[15],"based":[16],"on":[17],"their":[18],"perceived":[19],"quality.":[20],"AES":[21,50,92],"has":[22],"been":[23],"among":[24,169],"the":[25,62,84,148,156,166,170],"most":[26,157],"significant":[27],"Natural":[28],"Language":[29],"Processing":[30],"(NLP)":[31],"applications":[32],"due":[33],"its":[35],"educational":[36],"commercial":[38],"value.":[39],"Similar":[40],"many":[42],"other":[43,132,149],"NLP":[44],"tasks,":[45],"training":[46,105],"model":[48],"for":[49,91],"typically":[51],"acquiring":[53],"substantial":[55,70,128],"amount":[56],"of":[57,87],"labeled":[58,143],"data":[59,144],"specific":[60],"being":[64],"graded.":[65],"This":[66],"usually":[67],"incurs":[68],"cost.":[71],"In":[72],"this":[73],"study,":[74],"we":[75,95],"consider":[76],"two":[77],"recent":[78],"few-shot":[79,100,110,160],"learning":[80,89,101,111,161],"methods":[81,90],"enhance":[83],"predictive":[85],"performance":[86,129,139],"machine":[88],"tasks.":[93],"Specifically,":[94],"experiment":[96],"with":[97],"prompt-based":[99],"method,":[102],"pattern":[103],"exploiting":[104],"(PET),":[106],"prompt-free":[109],"strategy,":[112],"SetFit,":[113],"compare":[115],"these":[116],"against":[117],"vanilla":[118],"fine-tuning.":[119],"Our":[120],"numerical":[121],"study":[122],"shows":[123],"that":[124],"PET":[125,151],"can":[126,136],"provide":[127],"gains":[130],"over":[131],"methods,":[133],"it":[135],"effectively":[137],"boost":[138],"when":[140],"access":[141],"is":[145,152,165],"limited.":[146],"On":[147],"hand,":[150],"found":[153],"be":[155],"computationally":[158],"expensive":[159],"method":[162,168],"considered,":[163],"whileSetFit":[164],"fastest":[167],"approaches.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-01-24T23:23:39.755997","created_date":"2025-10-10T00:00:00"}