{"id":"https://openalex.org/W7166554187","doi":"https://doi.org/10.63317/35ytj3qcrwv4","title":"Evaluating Transformer Model Family Representations Through Automated Essay Scoring","display_name":"Evaluating Transformer Model Family Representations Through Automated Essay Scoring","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7166554187","doi":"https://doi.org/10.63317/35ytj3qcrwv4"},"language":"en","primary_location":{"id":"doi:10.63317/35ytj3qcrwv4","doi":"https://doi.org/10.63317/35ytj3qcrwv4","is_oa":true,"landing_page_url":"https://doi.org/10.63317/35ytj3qcrwv4","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/readixtsar/pdf/2026.readixtsar-1.11.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/readixtsar/pdf/2026.readixtsar-1.11.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139555268","display_name":"Akchay Ozten","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Akchay Ozten","raw_affiliation_strings":["University of Exeter"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034564787","display_name":"Rodrigo Wilkens","orcid":"https://orcid.org/0000-0003-4366-1215","observed_orcids":["https://orcid.org/0000-0003-4366-1215"]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rodrigo Wilkens","raw_affiliation_strings":["University of Exeter"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Exeter","institution_ids":["https://openalex.org/I23923803"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I23923803"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42411326,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"142","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8647000193595886,"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":0.8647000193595886,"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":0.05559999868273735,"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/T12488","display_name":"Mental Health via Writing","score":0.008100000210106373,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/automated-essay-scoring","display_name":"automated essay scoring","score":0.9980000257492065},{"id":"https://openalex.org/keywords/transformer-embeddings","display_name":"transformer embeddings","score":0.6639999747276306},{"id":"https://openalex.org/keywords/linguistic-features","display_name":"linguistic features","score":0.7139999866485596},{"id":"https://openalex.org/keywords/hybrid-model","display_name":"hybrid model","score":0.550000011920929},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.3490000069141388},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.609000027179718}],"concepts":[{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.7552000284194946},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7271999716758728},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6718000173568726},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6671000123023987},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5192000269889832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5094000101089478},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.49059998989105225},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34439998865127563},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.63317/35ytj3qcrwv4","doi":"https://doi.org/10.63317/35ytj3qcrwv4","is_oa":true,"landing_page_url":"https://doi.org/10.63317/35ytj3qcrwv4","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/readixtsar/pdf/2026.readixtsar-1.11.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.63317/35ytj3qcrwv4","doi":"https://doi.org/10.63317/35ytj3qcrwv4","is_oa":true,"landing_page_url":"https://doi.org/10.63317/35ytj3qcrwv4","pdf_url":"http://www.lrec-conf.org/proceedings/lrec2026/workshops/readixtsar/pdf/2026.readixtsar-1.11.pdf","source":{"id":"https://openalex.org/S7407088301","display_name":"Language Resources and Evaluation Conference (LREC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Language Resources and Evaluation Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.6288395524024963,"display_name":"Quality education","id":"https://metadata.un.org/sdg/4"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166554187.pdf","grobid_xml":"https://content.openalex.org/works/W7166554187.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3134748792","https://openalex.org/W4385571889","https://openalex.org/W7222045748","https://openalex.org/W4402854599","https://openalex.org/W3206253072","https://openalex.org/W3103252638","https://openalex.org/W7118807216","https://openalex.org/W7077890559","https://openalex.org/W7202363359","https://openalex.org/W2974340959"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"have":[3],"become":[4],"central":[5],"to":[6],"Automated":[7],"Essay":[8],"Scoring":[9],"(AES),":[10],"typically":[11],"through":[12],"fine-tuned":[13,121],"transformer":[14,46],"encoders":[15],"or":[16],"prompt-based":[17],"applications":[18],"of":[19,25,67,127],"decoder":[20,26,45,78,114],"models.However,":[21],"the":[22,65,125],"representational":[23],"capacity":[24],"models":[27,102],"as":[28],"frozen":[29,55,113],"embedding":[30],"extractors":[31],"remains":[32],"largely":[33],"unexplored.In":[34],"this":[35],"paper,":[36],"we":[37,53],"present":[38],"a":[39,120],"controlled":[40],"comparison":[41],"between":[42],"encoder":[43,82],"and":[44,64,94,100],"embeddings":[47,79,83,106,115],"for":[48],"prompt-agnostic":[49],"AES.Using":[50],"regression":[51],"models,":[52],"evaluate":[54],"representations":[56],"across":[57,90],"two":[58],"English":[59],"datasets.We":[60],"analyzed":[61],"scaling":[62],"effects":[63,97],"impact":[66],"integrating":[68],"explicit":[69],"linguistic":[70,108],"features":[71,109],"in":[72,84,130],"hybrid":[73,101],"configurations.Our":[74],"results":[75],"show":[76],"that":[77,103],"consistently":[80],"outperform":[81],"embedding-only":[85],"settings,":[86],"with":[87,107,119],"gains":[88],"generalizing":[89],"holistic":[91],"essay":[92,131],"scoring":[93],"proficiency":[95],"prediction.Scaling":[96],"are":[98],"modest,":[99],"combine":[104],"contextual":[105],"yield":[110],"further":[111],"improvements.Notably,":[112],"achieve":[116],"performance":[117],"competitive":[118],"BERT.These":[122],"findings":[123],"highlight":[124],"importance":[126],"representation-level":[128],"properties":[129],"scoring.":[132]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-06-30T00:00:00"}