{"id":"https://openalex.org/W7163376823","doi":"https://doi.org/10.48550/arxiv.2606.03078","title":"G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation","display_name":"G^2C-MT: Graph-Guided Context Selection for Document-Level Machine Translation","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163376823","doi":"https://doi.org/10.48550/arxiv.2606.03078"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03078","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.2606.03078","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137781952","display_name":"Baijun Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Baijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137749059","display_name":"Zixuan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137715922","display_name":"Xiangyu Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137719860","display_name":"Yu Liu (6938)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137728674","display_name":"Longbo Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Longbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017929716","display_name":"Rupu Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Rupu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011071806","display_name":"\u8d75\u535a\u9e3f","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Bohong","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":0.7549999952316284,"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":0.7549999952316284,"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":0.17059999704360962,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.010400000028312206,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.6279000043869019},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5543000102043152},{"id":"https://openalex.org/keywords/paragraph","display_name":"Paragraph","score":0.4975000023841858},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44999998807907104},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4345000088214874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8170999884605408},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6114000082015991},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5543000102043152},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.536899983882904},{"id":"https://openalex.org/C2777206241","wikidata":"https://www.wikidata.org/wiki/Q194431","display_name":"Paragraph","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4345000088214874},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4221999943256378},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39629998803138733},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.27549999952316284},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2628999948501587}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03078","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.2606.03078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03078","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":[{"score":0.5074986219406128,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Effective":[0],"document-level":[1],"machine":[2],"translation":[3,153],"(DocMT)":[4],"requires":[5],"capturing":[6],"long-range":[7],"discourse":[8,30,63,76],"dependencies.":[9],"Recent":[10],"work":[11],"has":[12],"explored":[13],"retrieval-based":[14],"and":[15,87,101,175],"discourse-aware":[16],"context":[17,52,69,118,125,144],"selection.":[18],"However,":[19],"these":[20],"approaches":[21],"often":[22],"lack":[23],"an":[24],"explicit":[25],"mechanism":[26],"for":[27,46,120,137,155],"modeling":[28],"structured":[29,56],"dependencies":[31],"between":[32,91],"distant":[33],"paragraphs":[34],"in":[35],"a":[36,55,61,85,107,116,132],"document.":[37],"In":[38,78],"this":[39],"paper,":[40],"we":[41,80,105],"propose":[42,106],"G^2C-MT":[43,165],"(Graph-Guided":[44],"Context":[45],"Machine":[47],"Translation),":[48],"which":[49,146],"views":[50],"DocMT":[51],"selection":[53],"as":[54,84],"path":[57,119,126],"discovery":[58],"problem":[59],"on":[60,73,169],"lightweight":[62],"graph,":[64],"rather":[65],"than":[66],"retrieving":[67],"unstructured":[68],"sets":[70],"or":[71],"relying":[72],"expensive":[74],"LLM-based":[75],"modeling.":[77],"detail,":[79],"represent":[81],"each":[82,92,121],"paragraph":[83],"node":[86],"model":[88,135],"the":[89,112,176],"relationship":[90],"pair":[93],"of":[94],"nodes,":[95],"considering":[96],"their":[97],"semantic":[98],"similarity,":[99],"adjacency,":[100],"keyword":[102],"overlap.":[103],"Furthermore,":[104],"depth-biased":[108],"random":[109],"walk":[110],"over":[111],"graph":[113],"to":[114,130],"sample":[115],"backward":[117],"target":[122],"paragraph.":[123],"The":[124],"will":[127],"be":[128],"used":[129],"prompt":[131],"large":[133],"language":[134],"(LLM)":[136],"translation.":[138],"This":[139],"framework":[140],"naturally":[141],"supports":[142],"multi-path":[143],"sampling,":[145],"can":[147],"improve":[148],"robustness":[149],"by":[150],"aggregating":[151],"diverse":[152],"candidates":[154],"discourse-ambiguous":[156],"inputs.":[157],"Experiments":[158],"conducted":[159],"across":[160],"various":[161],"domains":[162],"show":[163],"that":[164],"outperforms":[166],"strong":[167],"baselines":[168],"multiple":[170],"LLMs,":[171],"including":[172],"DeepSeek-V3,":[173],"Gemini-2.5-Flash-lite,":[174],"Qwen-2.5/3":[177],"series.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-04T00:00:00"}