{"id":"https://openalex.org/W7151778149","doi":"https://doi.org/10.48550/arxiv.2604.05684","title":"Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment","display_name":"Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7151778149","doi":"https://doi.org/10.48550/arxiv.2604.05684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.05684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133216801","display_name":"Seongtae Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Seongtae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133161387","display_name":"Youngjoon Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jang, Youngjoon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035555716","display_name":"Jungseob Lee","orcid":"https://orcid.org/0000-0002-9431-6342"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jungseob","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133182801","display_name":"Hyeonseok Moon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moon, Hyeonseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133193162","display_name":"Heuiseok Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Heuiseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9078999757766724,"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"}},"topics":[{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9078999757766724,"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/T10028","display_name":"Topic Modeling","score":0.01510000042617321,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.007799999788403511,"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/embedding","display_name":"Embedding","score":0.5238000154495239},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.5234000086784363},{"id":"https://openalex.org/keywords/multilingualism","display_name":"Multilingualism","score":0.38519999384880066},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.35510000586509705},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3084000051021576},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.2989000082015991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8248000144958496},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6819000244140625},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4413999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4099000096321106},{"id":"https://openalex.org/C2780035574","wikidata":"https://www.wikidata.org/wiki/Q30081","display_name":"Multilingualism","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C90288658","wikidata":"https://www.wikidata.org/wiki/Q3318149","display_name":"Human\u2013computer information retrieval","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C2778842860","wikidata":"https://www.wikidata.org/wiki/Q986551","display_name":"Cross-language information retrieval","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05684","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.7098656892776489,"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":{"With":[0],"the":[1,29,40,59,93,98,102,120,161,168,176,181],"increasing":[2],"accessibility":[3],"and":[4,38,107,115],"utilization":[5],"of":[6,31,36,124,154,185],"multilingual":[7,84,125,187],"documents,":[8],"Cross-Lingual":[9],"Information":[10],"Retrieval":[11],"(CLIR)":[12],"has":[13],"emerged":[14],"as":[15,101],"an":[16],"important":[17],"research":[18],"area.":[19],"Conventionally,":[20],"CLIR":[21],"tasks":[22],"have":[23],"been":[24],"conducted":[25],"under":[26,133],"settings":[27],"where":[28,76],"language":[30,100],"documents":[32,41,78,91],"differs":[33],"from":[34],"that":[35,54,175],"queries,":[37],"typically,":[39],"are":[42],"composed":[43],"in":[44,55,72,97],"a":[45,57,73,139,150],"single":[46],"coherent":[47],"language.":[48],"In":[49],"this":[50,109],"paper,":[51],"we":[52,69,111,137],"highlight":[53],"such":[56],"setting,":[58],"cross-lingual":[60,121,131,146,162,182],"alignment":[61,122,183],"capability":[62],"may":[63],"not":[64],"be":[65],"evaluated":[66],"adequately.":[67],"Specifically,":[68],"observe":[70],"that,":[71],"document":[74,95],"pool":[75],"English":[77,90,169],"coexist":[79],"with":[80],"another":[81],"language,":[82],"most":[83,186],"retrievers":[85],"tend":[86],"to":[87,118,129],"prioritize":[88],"unrelated":[89],"over":[92],"related":[94],"written":[96],"same":[99],"query.":[103],"To":[104],"rigorously":[105],"analyze":[106],"quantify":[108],"phenomenon,":[110],"introduce":[112],"various":[113],"scenarios":[114],"metrics":[116],"designed":[117],"evaluate":[119],"performance":[123,132,164],"retrieval":[126,163],"models.":[127,189],"Furthermore,":[128],"improve":[130],"these":[134],"challenging":[135],"conditions,":[136],"propose":[138],"novel":[140],"training":[141],"strategy":[142],"aimed":[143],"at":[144],"enhancing":[145],"alignment.":[147],"Using":[148],"only":[149],"small":[151],"dataset":[152],"consisting":[153],"2.8k":[155],"samples,":[156],"our":[157],"method":[158,178],"significantly":[159],"improves":[160],"while":[165],"simultaneously":[166],"mitigating":[167],"inclination":[170],"problem.":[171],"Extensive":[172],"analyses":[173],"demonstrate":[174],"proposed":[177],"substantially":[179],"enhances":[180],"capabilities":[184],"embedding":[188]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-09T00:00:00"}