{"id":"https://openalex.org/W7204177519","doi":"https://doi.org/10.18653/2026.emnlp-main.623","title":"Length-Adaptive Decoding for Masked Diffusion Machine Translation","display_name":"Length-Adaptive Decoding for Masked Diffusion Machine Translation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204177519","doi":"https://doi.org/10.18653/2026.emnlp-main.623"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.623","doi":"https://doi.org/10.18653/2026.emnlp-main.623","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.623","pdf_url":"https://aclanthology.org/2026.emnlp-main.623.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.623.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102201418","display_name":"Yan Zhan","orcid":"https://orcid.org/0009-0006-5416-6446","observed_orcids":["https://orcid.org/0009-0006-5416-6446"]},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhan","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148163786","display_name":"Mengkai Hou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengkai Hou","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148056846","display_name":"Wanting Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210133343","display_name":"BYD (China)","ror":"https://ror.org/03kkp9n92","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210133343"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanting Zhang","raw_affiliation_strings":["BYD Company Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BYD Company Limited","institution_ids":["https://openalex.org/I4210133343"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068509172","display_name":"Zhijun Gao","orcid":"https://orcid.org/0009-0001-0253-3180","observed_orcids":["https://orcid.org/0009-0001-0253-3180"]},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijun Gao","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55648666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"14306","last_page":"14327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9502000212669373,"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":0.9502000212669373,"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":0.03280000016093254,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.0035000001080334187,"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/masked-diffusion-language-models","display_name":"masked diffusion language models","score":0.925000011920929},{"id":"https://openalex.org/keywords/machine-translation","display_name":"machine translation","score":0.8040000200271606},{"id":"https://openalex.org/keywords/predictive-entropy","display_name":"predictive entropy","score":0.8700000047683716},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"noise reduction","score":0.6159999966621399},{"id":"https://openalex.org/keywords/chinese-english-translation","display_name":"Chinese-English translation","score":0.8489999771118164}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8790000081062317},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8562999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6632000207901001},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.558899998664856},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4778999984264374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46540001034736633},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44760000705718994},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.41530001163482666},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C116188536","wikidata":"https://www.wikidata.org/wiki/Q17118377","display_name":"Swift","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C21353171","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Length measurement","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2653999924659729},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-main.623","doi":"https://doi.org/10.18653/2026.emnlp-main.623","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.623","pdf_url":"https://aclanthology.org/2026.emnlp-main.623.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.22274","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22274","pdf_url":"https://arxiv.org/pdf/2608.22274","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.22274","doi":"https://doi.org/10.48550/arxiv.2608.22274","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.22274","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,"listed_in":[],"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"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.623","doi":"https://doi.org/10.18653/2026.emnlp-main.623","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.623","pdf_url":"https://aclanthology.org/2026.emnlp-main.623.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204177519.pdf","grobid_xml":"https://content.openalex.org/works/W7204177519.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W4401306886","https://openalex.org/W4416051103","https://openalex.org/W7102397876","https://openalex.org/W7127132446","https://openalex.org/W7127336425","https://openalex.org/W7128475253","https://openalex.org/W7134247832","https://openalex.org/W7147017306"],"related_works":["https://openalex.org/W7204908216","https://openalex.org/W7127090971","https://openalex.org/W7166638395","https://openalex.org/W4414990963","https://openalex.org/W2970690146","https://openalex.org/W4416156711","https://openalex.org/W7223132121","https://openalex.org/W7127132446","https://openalex.org/W7172505630","https://openalex.org/W7134844364"],"abstract_inverted_index":{"Machine":[0],"translation":[1,118],"tests":[2],"masked":[3,26,160],"diffusion":[4,27,161],"language":[5],"models":[6],"(dLLMs)":[7],"because":[8],"every":[9],"source":[10],"token":[11,32],"must":[12,20],"be":[13],"rendered":[14],"faithfully,":[15],"while":[16],"fixed":[17],"canvas":[18,71],"decoding":[19,28],"choose":[21],"target":[22,58,99,175],"length":[23,37,53,85,176],"before":[24],"denoising.Existing":[25],"work":[29],"mainly":[30],"studies":[31],"unmasking":[33],"order,":[34],"leaving":[35],"this":[36,159],"decision":[38],"underexplored":[39],"despite":[40],"its":[41],"direct":[42],"effect":[43],"on":[44,101,128,137,146,150],"coverage":[45],"and":[46,68,91,104,148],"redundancy.We":[47],"introduce":[48],"Entropy-Valley":[49],"(EV),":[50],"a":[51,80,131],"training-free":[52],"selector":[54],"that":[55,108],"scores":[56],"candidate":[57],"canvases":[59],"by":[60,116],"mean":[61],"predictive":[62],"entropy":[63],"from":[64,97],"all-mask":[65],"forward":[66],"passes":[67],"selects":[69],"the":[70,72,94,121,138,142,174],"backbone":[73],"is":[74,177],"most":[75],"prepared":[76],"to":[77,79,167],"fill.Relative":[78],"baseline":[81],"using":[82],"training":[83],"corpus":[84],"statistics,":[86],"EV":[87,143],"recovers":[88],"64.9%,":[89],"65.3%,":[90],"33.0%":[92],"of":[93],"COMET-22":[95],"gain":[96],"reference":[98,114],"lengths":[100,110],"En\u2192Zh,":[102],"Zh\u2192En,":[103],"En\u2192De.Our":[105],"diagnostics":[106],"show":[107],"denoising-friendly":[109],"need":[111],"not":[112],"match":[113],"lengths.Evaluation":[115],"three":[117],"experts":[119],"supports":[120],"En\u2194Zh":[122],"adequacy":[123],"gains,":[124],"with":[125,130],"stronger":[126],"evidence":[127],"Zh\u2192En.Compared":[129],"LLaMA-3-8B":[132],"autoregressive":[133],"(AR)":[134],"model":[135],"trained":[136],"same":[139],"fine-tuning":[140],"data,":[141],"system":[144],"ties":[145],"En\u2192Zh":[147],"leads":[149],"Zh\u2192En;":[151],"an":[152],"oracle-length":[153],"diagnostic":[154],"further":[155],"shows":[156],"that,":[157],"in":[158],"MT":[162],"setting,":[163],"deciding":[164],"which":[165],"tokens":[166],"reveal":[168],"first":[169],"matters":[170],"less":[171],"than":[172],"how":[173],"supplied.":[178]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-26T00:00:00"}