{"id":"https://openalex.org/W7140319300","doi":"https://doi.org/10.48550/arxiv.2603.22629","title":"LGSE: Lexically Grounded Subword Embedding Initialization for Low-Resource Language Adaptation","display_name":"LGSE: Lexically Grounded Subword Embedding Initialization for Low-Resource Language Adaptation","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140319300","doi":"https://doi.org/10.48550/arxiv.2603.22629"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22629","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095941310","display_name":"Hailay Kidu Teklehaymanot","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teklehaymanot, Hailay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040539907","display_name":"Dren Fazlija","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fazlija, Dren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074427964","display_name":"Wolfgang Nejdl","orcid":"https://orcid.org/0000-0003-3374-2193"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nejdl, Wolfgang","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/T10028","display_name":"Topic Modeling","score":0.3540000021457672,"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":0.3540000021457672,"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":0.22529999911785126,"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":0.18639999628067017,"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":0.7181000113487244},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6955999732017517},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5565000176429749},{"id":"https://openalex.org/keywords/morpheme","display_name":"Morpheme","score":0.5460000038146973},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4569999873638153},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.41659998893737793},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.397599995136261},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.38109999895095825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825000286102295},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6955999732017517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6823999881744385},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6412000060081482},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C165297611","wikidata":"https://www.wikidata.org/wiki/Q43249","display_name":"Morpheme","level":2,"score":0.5460000038146973},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38109999895095825},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C622187","wikidata":"https://www.wikidata.org/wiki/Q3500773","display_name":"BLEU","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22629","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.22629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22629","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adapting":[0],"pretrained":[1,82,134],"language":[2],"models":[3],"to":[4,105,140],"low-resource,":[5],"morphologically":[6,51,184,213],"rich":[7],"languages":[8],"remains":[9],"a":[10,89,114],"significant":[11],"challenge.":[12],"Existing":[13],"vocabulary":[14,156],"expansion":[15],"methods":[16,205],"typically":[17],"rely":[18],"on":[19,170],"arbitrarily":[20],"segmented":[21,93],"subword":[22,83],"units,":[23],"resulting":[24],"in":[25,182,221,228],"fragmented":[26],"lexical":[27],"representations":[28,104],"and":[29,75,157,159,179,189],"loss":[30],"of":[31,57,61,121,148,212],"critical":[32],"morphological":[33,192],"information.":[34,108],"To":[35,144],"address":[36],"this":[37],"limitation,":[38],"we":[39,112,150],"propose":[40],"the":[41,132,141,146,152,162,210,229],"Lexically":[42],"Grounded":[43],"Subword":[44],"Embedding":[45],"Initialization":[46],"(LGSE)":[47],"framework,":[48],"which":[49],"introduces":[50],"informed":[52],"segmentation":[53,193],"for":[54,217],"initializing":[55],"embeddings":[56,79,124,164],"novel":[58],"tokens.":[59],"Instead":[60],"using":[62,101],"random":[63],"vectors":[64],"or":[65,84],"arbitrary":[66],"subwords,":[67],"LGSE":[68,169,201],"decomposes":[69],"words":[70],"into":[71,94],"their":[72,126],"constituent":[73],"morphemes":[74],"constructs":[76],"semantically":[77],"coherent":[78],"by":[80],"averaging":[81],"FastText-based":[85],"morpheme":[86],"representations.":[87],"When":[88],"token":[90],"cannot":[91],"be":[92],"meaningful":[95],"morphemes,":[96],"its":[97],"embedding":[98,135,215],"is":[99],"constructed":[100],"character":[102],"n-gram":[103],"capture":[106],"structural":[107],"During":[109],"Language-Adaptive":[110],"Pretraining,":[111],"apply":[113],"regularization":[115],"term":[116],"that":[117,200],"penalizes":[118],"large":[119],"deviations":[120],"newly":[122],"introduced":[123],"from":[125],"initialized":[127],"values,":[128],"preserving":[129],"alignment":[130],"with":[131],"original":[133,153],"space":[136],"while":[137],"enabling":[138],"adaptation":[139],"target":[142],"language.":[143],"isolate":[145],"effect":[147],"initialization,":[149],"retain":[151],"pre-trained":[154],"model":[155],"tokenizer":[158],"update":[160],"only":[161],"new":[163],"during":[165],"adaptation.":[166],"We":[167],"evaluate":[168],"three":[171],"NLP":[172],"tasks:":[173],"Question":[174],"Answering,":[175],"Named":[176],"Entity":[177],"Recognition,":[178],"Text":[180],"Classification,":[181],"two":[183],"rich,":[185],"low-resource":[186],"languages:":[187],"Amharic":[188],"Tigrinya,":[190],"where":[191],"resources":[194,225],"are":[195,226],"available.":[196],"Experimental":[197],"results":[198],"show":[199],"consistently":[202],"outperforms":[203],"baseline":[204],"across":[206],"all":[207],"tasks,":[208],"demonstrating":[209],"effectiveness":[211],"grounded":[214],"initialization":[216],"improving":[218],"representation":[219],"quality":[220],"underrepresented":[222],"languages.":[223],"Project":[224],"available":[227],"GitHub":[230],"link.":[231]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-26T00:00:00"}