{"id":"https://openalex.org/W7163998943","doi":"https://doi.org/10.48550/arxiv.2606.09767","title":"Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan","display_name":"Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7163998943","doi":"https://doi.org/10.48550/arxiv.2606.09767"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09767","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.09767","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138222264","display_name":"Alexander Chulzhanov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chulzhanov, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138256492","display_name":"Soeren Eberhardt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eberhardt, Soeren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078060919","display_name":"Arjun Mukherjee","orcid":"https://orcid.org/0000-0002-8896-604X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mukherjee, Arjun","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/T10181","display_name":"Natural Language Processing Techniques","score":0.48089998960494995,"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":0.48089998960494995,"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/T12090","display_name":"Language and cultural evolution","score":0.10019999742507935,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.04670000076293945,"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/bootstrapping","display_name":"Bootstrapping (finance)","score":0.5684999823570251},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5680000185966492},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.532800018787384},{"id":"https://openalex.org/keywords/agglutinative-language","display_name":"Agglutinative language","score":0.45989999175071716},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.39879998564720154},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3596000075340271},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.35499998927116394},{"id":"https://openalex.org/keywords/texture-synthesis","display_name":"Texture synthesis","score":0.30070000886917114},{"id":"https://openalex.org/keywords/glossary","display_name":"Glossary","score":0.29269999265670776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7638999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6507999897003174},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6039999723434448},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.532800018787384},{"id":"https://openalex.org/C80875076","wikidata":"https://www.wikidata.org/wiki/Q171263","display_name":"Agglutinative language","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C50494287","wikidata":"https://www.wikidata.org/wiki/Q658467","display_name":"Texture synthesis","level":5,"score":0.30070000886917114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30000001192092896},{"id":"https://openalex.org/C2780031656","wikidata":"https://www.wikidata.org/wiki/Q859161","display_name":"Glossary","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C89421646","wikidata":"https://www.wikidata.org/wiki/Q843632","display_name":"Sequent","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C157659113","wikidata":"https://www.wikidata.org/wiki/Q533822","display_name":"WordNet","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09767","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.09767","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09767","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":0.7084940075874329,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"machine":[1],"translation":[2],"for":[3,165,175,200],"digitally":[4],"low-resource":[5],"Indigenous":[6],"languages":[7],"is":[8,190],"often":[9],"hindered":[10],"by":[11],"extreme":[12],"data":[13,22,28,199],"scarcity,":[14],"prompting":[15],"reliance":[16],"on":[17,41,60],"extractive":[18],"web-scraping.":[19],"To":[20],"ensure":[21],"sovereignty,":[23],"this":[24],"study":[25,150],"introduces":[26],"a":[27,49,92,152,191],"synthesis":[29],"methodology":[30],"to":[31,115],"bootstrap":[32],"NMT":[33],"models":[34],"without":[35],"scraping":[36],"target-language":[37],"parallel":[38],"text.":[39],"Focusing":[40],"Q'eqchi'":[42],"Mayan,":[43],"we":[44,185],"transformed":[45],"community-sourced":[46],"dictionaries":[47],"into":[48,143],"massive":[50],"synthetic":[51,74,122,176,188],"corpus,":[52],"utilizing":[53,151],"Parameter-Efficient":[54],"Fine-Tuning":[55],"(PEFT)":[56],"via":[57,203],"LoRA":[58,171],"adapters":[59],"an":[61,88,148],"mT5-base":[62],"model.":[63],"In-domain":[64],"evaluation":[65,86],"demonstrates":[66],"high":[67,125],"structural":[68,118,194],"acquisition":[69],"(BLEU":[70,95],"42.02),":[71],"proving":[72],"that":[73,161,187],"constraints":[75],"effectively":[76],"teach":[77],"complex":[78],"agglutinative":[79],"morphology":[80],"and":[81],"VOS":[82],"word":[83],"order.":[84],"However,":[85],"against":[87],"organic":[89,141,182],"glossary":[90],"reveals":[91],"structural-semantic":[93],"gap":[94],"0.59),":[96],"where":[97],"the":[98,105,116,121,129,134,170,179],"model":[99,112],"maintains":[100],"grammatical":[101],"integrity":[102],"but":[103,196],"lacks":[104],"lexical":[106],"grounding":[107],"of":[108,120,137,181],"natural":[109,138],"language.":[110],"The":[111],"exhibits":[113],"overfitting":[114],"constrained":[117],"variance":[119],"templates;":[123],"despite":[124],"semantic":[126,201],"entropy":[127],"in":[128,157],"pipeline,":[130],"it":[131],"struggles":[132],"with":[133],"syntactic":[135],"fluidity":[136],"language,":[139],"forcing":[140],"inputs":[142],"rigid":[144],"learned":[145],"patterns.":[146],"Furthermore,":[147],"ablation":[149],"Multi-Task":[153],"Learning":[154],"architecture":[155],"resulted":[156],"negative":[158],"transfer,":[159],"suggesting":[160],"auxiliary":[162],"tasks":[163],"competed":[164],"limited":[166],"parameter":[167],"capacity":[168],"within":[169],"adapters,":[172],"causing":[173],"over-optimization":[174],"markers":[177],"at":[178],"expense":[180],"flexibility.":[183],"Ultimately,":[184],"establish":[186],"bootstrapping":[189],"highly":[192],"effective":[193],"primer,":[195],"requires":[197],"authentic":[198],"refinement":[202],"Curriculum":[204],"Learning.":[205]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}
