{"id":"https://openalex.org/W4416035429","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1482","title":"LiTransProQA: An LLM-based Literary Translation Evaluation Metric with Professional Question Answering","display_name":"LiTransProQA: An LLM-based Literary Translation Evaluation Metric with Professional Question Answering","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416035429","doi":"https://doi.org/10.18653/v1/2025.emnlp-main.1482"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1482","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1482","pdf_url":"https://aclanthology.org/2025.emnlp-main.1482.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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.emnlp-main.1482.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005943652","display_name":"Ran Zhang","orcid":"https://orcid.org/0000-0002-8967-5607"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ran Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101710970","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0003-0865-1484"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049195161","display_name":"Lieve Macken","orcid":"https://orcid.org/0000-0001-7516-7487"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lieve Macken","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053947568","display_name":"Steffen Eger","orcid":"https://orcid.org/0000-0003-4663-8336"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Steffen Eger","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5005943652"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3431,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91707381,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"29087","last_page":"29109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.3407999873161316,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.3407999873161316,"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/T10028","display_name":"Topic Modeling","score":0.3255999982357025,"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/T13629","display_name":"Text Readability and Simplification","score":0.15160000324249268,"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/question-answering","display_name":"Question answering","score":0.6003000140190125},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.535099983215332},{"id":"https://openalex.org/keywords/translation","display_name":"Translation (biology)","score":0.39169999957084656},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.27489998936653137},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.2676999866962433}],"concepts":[{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5526999831199646},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.535099983215332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5149000287055969},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.5016000270843506},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.487199991941452},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.39169999957084656},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2025.emnlp-main.1482","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1482","pdf_url":"https://aclanthology.org/2025.emnlp-main.1482.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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:01K9AVGVF5HRK38XFQZ890M9B8","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-01K9AVGVF5HRK38XFQZ890M9B8","pdf_url":"https://biblio.ugent.be/publication/01K9AVGVF5HRK38XFQZ890M9B8/file/01K9AVTKBNCNMT46K6MTMETKPN","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9798891763326","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.emnlp-main.1482","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.emnlp-main.1482","pdf_url":"https://aclanthology.org/2025.emnlp-main.1482.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 2025 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G352791218","display_name":null,"funder_award_id":"(BMBF)","funder_id":"https://openalex.org/F4320321114","funder_display_name":"Bundesministerium f\u00fcr Bildung und Forschung"},{"id":"https://openalex.org/G6052429835","display_name":null,"funder_award_id":"(DFG)","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6537435729","display_name":null,"funder_award_id":"Heisenberg","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416035429.pdf","grobid_xml":"https://content.openalex.org/works/W4416035429.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"impact":[1],"of":[2,157],"Large":[3],"Language":[4],"Models":[5],"(LLMs)":[6],"has":[7],"extended":[8],"into":[9],"literary":[10,63,77,90,99,104,199],"domains.However,":[11],"existing":[12],"evaluation":[13,64,112],"metrics":[14,138],"for":[15,60,76,197],"literature":[16],"prioritize":[17],"mechanical":[18],"accuracy":[19],"over":[20,140],"artistic":[21],"expression":[22],"and":[23,52,92,108,133,187,194],"tend":[24],"to":[25,32,56,85,128,166,182,206],"overrate":[26],"machine":[27],"translation":[28,34,50,78],"as":[29,103,149,191],"being":[30],"superior":[31],"human":[33],"from":[35,88],"experienced":[36,176],"professionals.In":[37],"the":[38,57,83,135,155],"long":[39],"run,":[40],"this":[41],"bias":[42],"could":[43],"result":[44],"in":[45,49,82,98,131,143],"an":[46,162,192],"irreversible":[47],"decline":[48],"quality":[51,100],"cultural":[53,106],"authenticity.In":[54],"response":[55],"urgent":[58],"need":[59],"a":[61,69],"specialized":[62],"metric,":[65],"we":[66],"introduce":[67],"LITRANSPROQA,":[68],"novel,":[70],"referencefree,":[71],"LLM-based":[72],"question-answering":[73],"framework":[74],"designed":[75],"evaluation.LITRANSPROQA":[79],"integrates":[80],"humans":[81],"loop":[84],"incorporate":[86],"insights":[87,148],"professional":[89,146,177],"translators":[91],"researchers,":[93],"focusing":[94],"on":[95],"critical":[96],"elements":[97],"assessment":[101],"such":[102],"devices,":[105],"understanding,":[107],"authorial":[109],"voice.Our":[110],"extensive":[111],"shows":[113,179],"that":[114,201],"while":[115],"literaryfinetuned":[116],"XCOMET-XL":[117],"yields":[118],"marginal":[119],"gains,":[120],"LITRANSPROQA":[121,160],"substantially":[122],"outperforms":[123],"current":[124],"metrics,":[125],"achieving":[126],"up":[127],"0.07":[129],"gain":[130],"correlation":[132],"surpassing":[134],"best":[136],"state-of-theart":[137],"by":[139],"15":[141],"points":[142],"adequacy":[144,163],"assessments.Incorporating":[145],"translator":[147,158],"weights":[150],"further":[151],"improves":[152],"performance,":[153],"highlighting":[154],"value":[156],"inputs.Notably,":[159],"reaches":[161],"performance":[164],"comparable":[165],"trained":[167],"linguistic":[168],"student":[169],"evaluators,":[170],"though":[171],"it":[172],"still":[173],"falls":[174],"behind":[175],"translators.LITRANSPROQA":[178],"broad":[180],"applicability":[181],"open-source":[183],"models":[184],"like":[185],"LLaMA3.3-70b":[186],"Qwen2.5-32b,indicating":[188],"its":[189],"potential":[190],"accessible":[193],"training-free":[195],"tool":[196],"evaluating":[198],"translations":[200],"require":[202],"local":[203],"processing":[204],"due":[205],"copyright":[207],"or":[208],"ethical":[209],"considerations.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-11-08T00:00:00"}