{"id":"https://openalex.org/W7162455411","doi":"https://doi.org/10.48550/arxiv.2605.25165","title":"Multilingual Humour-Aware Retrieval with Dense and Re-Ranking Models","display_name":"Multilingual Humour-Aware Retrieval with Dense and Re-Ranking Models","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W7162455411","doi":"https://doi.org/10.48550/arxiv.2605.25165"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.25165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103169113","display_name":"Georgios Arampatzis","orcid":"https://orcid.org/0000-0003-0674-6260"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arampatzis, Georgios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058891144","display_name":"Avi Arampatzis","orcid":"https://orcid.org/0000-0003-2415-4592"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arampatzis, Avi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11795","display_name":"Humor Studies and Applications","score":0.679099977016449,"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"}},"topics":[{"id":"https://openalex.org/T11795","display_name":"Humor Studies and Applications","score":0.679099977016449,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.13940000534057617,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.020800000056624413,"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/clef","display_name":"Clef","score":0.8694000244140625},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5954999923706055},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5418000221252441},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4876999855041504},{"id":"https://openalex.org/keywords/multilingualism","display_name":"Multilingualism","score":0.44920000433921814},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.41179999709129333},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.4077000021934509}],"concepts":[{"id":"https://openalex.org/C107763842","wikidata":"https://www.wikidata.org/wiki/Q181040","display_name":"Clef","level":3,"score":0.8694000244140625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7221999764442444},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6126999855041504},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5954999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5601999759674072},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5418000221252441},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4876999855041504},{"id":"https://openalex.org/C2780035574","wikidata":"https://www.wikidata.org/wiki/Q30081","display_name":"Multilingualism","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.447299987077713},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.41179999709129333},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C35219183","wikidata":"https://www.wikidata.org/wiki/Q5146","display_name":"Portuguese","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3517000079154968},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.31790000200271606},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.28949999809265137},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2777776507","wikidata":"https://www.wikidata.org/wiki/Q4807054","display_name":"Lexico","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8564398884773254,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Humour-aware":[0],"information":[1,40],"retrieval":[2,41,53,64],"poses":[3],"unique":[4],"challenges":[5,185],"beyond":[6],"standard":[7],"semantic":[8,130],"retrieval,":[9,135],"as":[10,26],"systems":[11],"must":[12],"account":[13],"not":[14,145],"only":[15],"for":[16,21,133],"topical":[17],"relevance":[18,189],"but":[19],"also":[20],"humour-specific":[22,85],"linguistic":[23],"phenomena":[24],"such":[25],"wordplay,":[27],"phonetic":[28],"ambiguity,":[29],"and":[30,56,104,164,177,180],"polysemy.":[31],"In":[32],"this":[33,157],"paper,":[34],"Team":[35,171],"DUTH":[36,172],"studies":[37],"multilingual":[38,61,149,175],"humour-aware":[39,188],"using":[42],"the":[43,76,94,108,126,170,184,191],"CLEF":[44],"2025":[45],"JOKER":[46,192],"Task":[47],"1":[48],"benchmark,":[49],"which":[50,79],"evaluates":[51],"humour":[52,134,138,167],"in":[54,72,166],"English":[55,109],"Portuguese.":[57],"Our":[58],"approach":[59],"combines":[60],"XLM-RoBERTa-based":[62],"dense":[63,131],"with":[65,114],"additional":[66],"system":[67],"variants,":[68],"including":[69,159],"neural":[70],"re-ranking,":[71],"order":[73],"to":[74,78,156],"assess":[75],"extent":[77],"general-purpose":[80],"Transformer":[81],"models":[82],"can":[83],"capture":[84],"relevance.":[86],"The":[87],"results":[88],"reveal":[89],"substantial":[90],"cross-lingual":[91],"variation.":[92],"While":[93],"Portuguese":[95],"runs":[96,110],"demonstrate":[97],"comparatively":[98],"strong":[99],"performance":[100],"across":[101],"MAP,":[102],"MRR,":[103],"early":[105],"precision":[106],"metrics,":[107],"perform":[111],"significantly":[112],"worse,":[113],"relevant":[115],"humorous":[116],"documents":[117],"frequently":[118],"appearing":[119],"at":[120],"lower":[121],"ranks.":[122],"These":[123],"findings":[124],"highlight":[125],"limitations":[127],"of":[128,186],"purely":[129],"representations":[132],"particularly":[136],"when":[137],"depends":[139],"on":[140],"surface-level":[141],"cues":[142],"that":[143],"are":[144],"explicitly":[146],"modelled":[147],"by":[148],"encoders.":[150],"We":[151],"further":[152],"analyse":[153],"contributing":[154],"factors":[155],"discrepancy,":[158],"dataset":[160],"characteristics,":[161],"query-document":[162],"alignment,":[163],"variation":[165],"mechanisms.":[168],"Overall,":[169],"experiments":[173],"establish":[174],"dense-retrieval":[176],"re-ranking":[178],"baselines":[179],"provide":[181],"insights":[182],"into":[183],"modelling":[187],"within":[190],"framework.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
