{"id":"https://openalex.org/W2948140294","doi":"https://doi.org/10.18653/v1/p19-1282","title":"Is Attention Interpretable?","display_name":"Is Attention Interpretable?","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2948140294","doi":"https://doi.org/10.18653/v1/p19-1282","mag":"2948140294"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1282","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1282","pdf_url":"https://www.aclweb.org/anthology/P19-1282.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1282.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045156473","display_name":"Sofia Serrano","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sofia Serrano","raw_affiliation_strings":["University of Washington ;"],"affiliations":[{"raw_affiliation_string":"University of Washington ;","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088517824","display_name":"Noah A. Smith","orcid":"https://orcid.org/0000-0002-2310-6380"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I4210156221","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Noah A. Smith","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA, USA","University of Washington ;"],"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA, USA","institution_ids":["https://openalex.org/I4210156221"]},{"raw_affiliation_string":"University of Washington ;","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045156473"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":17.66604074,"has_fulltext":true,"cited_by_count":115,"citation_normalized_percentile":{"value":0.99257224,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2931","last_page":"2951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9991999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9984999895095825,"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/computer-science","display_name":"Computer science","score":0.6196335554122925},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5724473595619202},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5102031230926514},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.49715831875801086},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4232955276966095},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.4184871017932892},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3885897397994995},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3135063648223877},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22868612408638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6196335554122925},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5724473595619202},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5102031230926514},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.49715831875801086},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4232955276966095},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.4184871017932892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3885897397994995},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3135063648223877},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22868612408638},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/p19-1282","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1282","pdf_url":"https://www.aclweb.org/anthology/P19-1282.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1906.03731","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.03731","pdf_url":"https://arxiv.org/pdf/1906.03731","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2948140294","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1906.03731v1","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1906.03731","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1906.03731","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:10.18653/v1/p19-1282","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1282","pdf_url":"https://www.aclweb.org/anthology/P19-1282.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2948140294.pdf","grobid_xml":"https://content.openalex.org/works/W2948140294.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1544827683","https://openalex.org/W1601924930","https://openalex.org/W1832693441","https://openalex.org/W1902237438","https://openalex.org/W2142972908","https://openalex.org/W2144933361","https://openalex.org/W2267186426","https://openalex.org/W2282821441","https://openalex.org/W2296540674","https://openalex.org/W2439568532","https://openalex.org/W2470673105","https://openalex.org/W2473286717","https://openalex.org/W2562607067","https://openalex.org/W2562979205","https://openalex.org/W2586597293","https://openalex.org/W2597655663","https://openalex.org/W2741040846","https://openalex.org/W2752392984","https://openalex.org/W2760327630","https://openalex.org/W2766912318","https://openalex.org/W2769063188","https://openalex.org/W2788142552","https://openalex.org/W2804878416","https://openalex.org/W2809671526","https://openalex.org/W2891612330","https://openalex.org/W2934842096","https://openalex.org/W2949780682","https://openalex.org/W2950524402","https://openalex.org/W2962816513","https://openalex.org/W2963012544","https://openalex.org/W2963069010","https://openalex.org/W2963123301","https://openalex.org/W2963233086","https://openalex.org/W2963403868","https://openalex.org/W2963506925","https://openalex.org/W2963798744","https://openalex.org/W2963899396","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2963403868","https://openalex.org/W2963341956","https://openalex.org/W2964308564","https://openalex.org/W2934842096","https://openalex.org/W2970726176","https://openalex.org/W2282821441","https://openalex.org/W2972324944","https://openalex.org/W2064675550","https://openalex.org/W2964121744","https://openalex.org/W2965373594","https://openalex.org/W2962851944","https://openalex.org/W2594633041","https://openalex.org/W2250539671","https://openalex.org/W1902237438","https://openalex.org/W1787224781","https://openalex.org/W2946417913","https://openalex.org/W2950577311","https://openalex.org/W2594475271","https://openalex.org/W2562979205","https://openalex.org/W2605409611"],"abstract_inverted_index":{"Attention":[0],"mechanisms":[1],"have":[2],"recently":[3],"boosted":[4],"performance":[5],"on":[6,79],"a":[7,119,126],"range":[8],"of":[9,97],"NLP":[10],"tasks.":[11],"Because":[12],"attention":[13,26,50,73,98,111],"layers":[14],"explicitly":[15],"weight":[16],"input":[17,114],"components'":[18,115],"representations,":[19],"it":[20,121],"is":[21,122],"also":[22,83],"often":[23],"assumed":[24],"that":[25,33,45,109],"can":[27],"be":[28],"used":[29],"to":[30,118],"identify":[31],"information":[32],"models":[34,56],"found":[35],"important":[36],"(e.g.,":[37],"specific":[38],"contextualized":[39],"word":[40],"tokens).":[41],"We":[42,107],"test":[43],"whether":[44],"assumption":[46],"holds":[47],"by":[48,123],"manipulating":[49],"weights":[51,74,99],"in":[52,62,70,87],"already-trained":[53],"text":[54],"classification":[55],"and":[57],"analyzing":[58],"the":[59],"resulting":[60],"differences":[61],"their":[63,102,105],"predictions.":[64],"While":[65],"we":[66,82],"observe":[67],"some":[68],"ways":[69,86],"which":[71,88],"higher":[72],"correlate":[75],"with":[76],"greater":[77],"impact":[78],"model":[80],"predictions,":[81],"find":[84],"many":[85],"this":[89],"does":[90],"not":[91],"hold,":[92],"i.e.,":[93],"where":[94],"gradient-based":[95],"rankings":[96],"better":[100],"predict":[101],"effects":[103],"than":[104],"magnitudes.":[106],"conclude":[108],"while":[110],"noisily":[112],"predicts":[113],"overall":[116],"importance":[117],"model,":[120],"no":[124],"means":[125],"fail-safe":[127],"indicator.":[128]},"counts_by_year":[{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":53},{"year":2020,"cited_by_count":45},{"year":2019,"cited_by_count":9}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}