{"id":"https://openalex.org/W7135243323","doi":"https://doi.org/10.48550/arxiv.2603.11342","title":"Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation","display_name":"Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135243323","doi":"https://doi.org/10.48550/arxiv.2603.11342"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11342","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11342","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":null,"license_id":null,"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.2603.11342","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084787300","display_name":"Aria Nourbakhsh","orcid":"https://orcid.org/0009-0007-2233-3155"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nourbakhsh, Aria","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070022854","display_name":"Salima Lamsiyah","orcid":"https://orcid.org/0000-0001-8789-5713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lamsiyah, Salima","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128960510","display_name":"Adelaide Danilov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Danilov, Adelaide","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055092426","display_name":"Christoph Schommer","orcid":"https://orcid.org/0000-0002-0308-7637"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schommer, Christoph","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5084787300"],"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8123000264167786,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8123000264167786,"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":0.050200000405311584,"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":0.029200000688433647,"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/attribution","display_name":"Attribution","score":0.8539000153541565},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.675599992275238},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5317000150680542},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.5047000050544739},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.33480000495910645},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3212999999523163}],"concepts":[{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.8539000153541565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7087000012397766},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.675599992275238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6500999927520752},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4526999890804291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4194999933242798},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3212999999523163},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C3020202489","wikidata":"https://www.wikidata.org/wiki/Q2032038","display_name":"Authorship attribution","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2705000042915344}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11342","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11342","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.11342","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11342","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7414642572402954}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,253],"study":[1],"of":[2,5,11,19,39,82,113,245],"the":[3,9,34,80,87,94,110,151,223,231,234,240,250],"attribution":[4,66,84,99,189,225,238],"input":[6],"features":[7],"to":[8,30,73,90,162,178,221],"output":[10],"neural":[12],"network":[13],"models":[14,45],"is":[15,46,248],"an":[16,212,243],"active":[17],"area":[18],"research.":[20],"While":[21],"numerous":[22],"Explainable":[23],"AI":[24],"(XAI)":[25],"techniques":[26],"have":[27],"been":[28],"proposed":[29],"interpret":[31],"these":[32,40,107],"models,":[33,140],"systematic":[35],"and":[36,78,105,125,134,138,144,180,194,203],"automated":[37],"evaluation":[38],"methods":[41,58,85,168,190],"in":[42,59,154,159,206],"sequence-to-sequence":[43],"(seq2seq)":[44],"less":[47,181],"explored.":[48],"This":[49],"paper":[50],"introduces":[51],"a":[52,69,75,114,217],"new":[53],"approach":[54],"for":[55,249],"evaluating":[56],"explainability":[57],"transformer-based":[60],"seq2seq":[61,207],"models.":[62,208],"We":[63],"use":[64],"teacher-derived":[65],"maps":[67,247],"as":[68],"structured":[70],"side":[71],"signal":[72],"guide":[74],"student":[76,115],"model,":[77],"quantify":[79],"utility":[81],"different":[83,188],"through":[86],"student's":[88],"ability":[89],"simulate":[91],"targets.":[92],"Using":[93],"Inseq":[95],"library,":[96],"we":[97,210],"extract":[98],"scores":[100,108],"over":[101],"source-target":[102,218],"sequence":[103],"pairs":[104,130],"inject":[106],"into":[109],"attention":[111],"mechanism":[112],"transformer":[116,214],"model":[117],"under":[118],"four":[119],"composition":[120],"operators":[121],"(addition,":[122],"multiplication,":[123],"averaging,":[124],"replacement).":[126],"Across":[127],"three":[128],"language":[129],"(de-en,":[131],"fr-en,":[132],"ar-en)":[133],"attributions":[135,197],"from":[136],"Marian-MT":[137],"mBART":[139],"Attention,":[141],"Value":[142],"Zeroing,":[143],"Layer":[145],"Gradient":[146],"$\\times$":[147,174],"Activation":[148],"consistently":[149],"yield":[150],"largest":[152],"gains":[153],"BLEU":[155],"(and":[156],"corresponding":[157],"improvements":[158],"chrF)":[160],"relative":[161],"baselines.":[163],"In":[164],"contrast,":[165],"other":[166],"gradient-based":[167],"(Saliency,":[169],"Integrated":[170],"Gradients,":[171],"DeepLIFT,":[172],"Input":[173],"Gradient,":[175],"GradientShap)":[176],"lead":[177],"smaller":[179],"consistent":[182],"improvements.":[183],"These":[184],"results":[185],"suggest":[186],"that":[187,195,230],"capture":[191,199],"distinct":[192],"signals":[193],"attention-derived":[196],"better":[198],"alignment":[200],"between":[201],"source":[202,254],"target":[204],"representations":[205],"Finally,":[209],"introduce":[211],"Attributor":[213,235],"that,":[215],"given":[216],"pair,":[219],"learns":[220],"reconstruct":[222],"teacher's":[224],"map.":[226],"Our":[227],"findings":[228],"demonstrate":[229],"more":[232,241],"accurately":[233],"can":[236,256],"reproduce":[237],"maps,":[239],"useful":[242],"injection":[244],"those":[246],"downstream":[251],"task.":[252],"code":[255],"be":[257],"found":[258],"on":[259],"GitHub.":[260]},"counts_by_year":[],"updated_date":"2026-03-14T06:46:50.379900","created_date":"2026-03-14T00:00:00"}
