{"id":"https://openalex.org/W2724701856","doi":"https://doi.org/10.18653/v1/d17-1042","title":"A causal framework for explaining the predictions of black-box sequence-to-sequence models","display_name":"A causal framework for explaining the predictions of black-box sequence-to-sequence models","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2724701856","doi":"https://doi.org/10.18653/v1/d17-1042","mag":"2724701856"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1042","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1042","pdf_url":"https://www.aclweb.org/anthology/D17-1042.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","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/D17-1042.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110947683","display_name":"David Alvarez-Melis","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"David Alvarez-Melis","raw_affiliation_strings":["CSAIL, MIT","Massachusetts Institute of Technology, Cambridge, United States"],"affiliations":[{"raw_affiliation_string":"CSAIL, MIT","institution_ids":[]},{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, United States","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048915657","display_name":"Tommi Jaakkola","orcid":"https://orcid.org/0000-0002-2199-0379"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tommi Jaakkola","raw_affiliation_strings":["CSAIL, MIT","Massachusetts Institute of Technology, Cambridge, United States"],"affiliations":[{"raw_affiliation_string":"CSAIL, MIT","institution_ids":[]},{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge, United States","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110947683"],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":4.5641,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.95643834,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"412","last_page":"421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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":0.9995999932289124,"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":0.998199999332428,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9972000122070312,"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/sequence","display_name":"Sequence (biology)","score":0.792162299156189},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.7445054650306702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6271378397941589},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5106919407844543},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.48072531819343567},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42319774627685547},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.42232686281204224},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3965340852737427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3526316285133362},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.17614132165908813}],"concepts":[{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.792162299156189},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.7445054650306702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6271378397941589},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5106919407844543},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.48072531819343567},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42319774627685547},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42232686281204224},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3965340852737427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3526316285133362},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.17614132165908813},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.18653/v1/d17-1042","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1042","pdf_url":"https://www.aclweb.org/anthology/D17-1042.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1707.01943","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1707.01943","pdf_url":"https://arxiv.org/pdf/1707.01943","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":null,"raw_type":"text"},{"id":"mag:2724701856","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1707.01943.pdf","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":"pmh:oai:dash.harvard.edu:1/42717548","is_oa":false,"landing_page_url":"https://dash.harvard.edu/handle/1/42717548","pdf_url":null,"source":{"id":"https://openalex.org/S4306401540","display_name":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I136199984","host_organization_name":"Harvard University","host_organization_lineage":["https://openalex.org/I136199984"],"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":"Conference Proceedings"},{"id":"doi:10.48550/arxiv.1707.01943","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1707.01943","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/d17-1042","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1042","pdf_url":"https://www.aclweb.org/anthology/D17-1042.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 2017 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321739","display_name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","ror":"https://ror.org/059ex5q34"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2724701856.pdf","grobid_xml":"https://content.openalex.org/works/W2724701856.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W635530177","https://openalex.org/W1503398984","https://openalex.org/W1909320841","https://openalex.org/W1996796871","https://openalex.org/W2045321028","https://openalex.org/W2130158951","https://openalex.org/W2133576408","https://openalex.org/W2282821441","https://openalex.org/W2439568532","https://openalex.org/W2440159969","https://openalex.org/W2538358357","https://openalex.org/W2574872930","https://openalex.org/W2594475271","https://openalex.org/W2893425640","https://openalex.org/W2962965405","https://openalex.org/W2963090522"],"related_works":["https://openalex.org/W2440159969","https://openalex.org/W2282821441","https://openalex.org/W2594475271","https://openalex.org/W2562979205","https://openalex.org/W2962851944","https://openalex.org/W1787224781","https://openalex.org/W2964159778","https://openalex.org/W1951216520","https://openalex.org/W2964308564","https://openalex.org/W2964153729","https://openalex.org/W2963207607","https://openalex.org/W2657631929","https://openalex.org/W2605409611","https://openalex.org/W1577394698","https://openalex.org/W2183167733","https://openalex.org/W180050721","https://openalex.org/W3044823215","https://openalex.org/W3095351248","https://openalex.org/W3035178110","https://openalex.org/W3081765391"],"abstract_inverted_index":{"We":[0,62,79],"interpret":[1],"the":[2,37,49,58,64],"predictions":[3],"of":[4,22,24],"any":[5],"blackbox":[6],"structured":[7],"input-structured":[8],"output":[9],"model":[10,39],"around":[11],"a":[12,44,53,71],"specific":[13],"input-output":[14,25],"pair.":[15],"Our":[16],"method":[17,82],"returns":[18],"an":[19],"\"explanation\"":[20],"consisting":[21],"groups":[23],"tokens":[26,47],"that":[27],"are":[28,33],"causally":[29],"related.":[30],"These":[31],"dependencies":[32],"inferred":[34],"by":[35],"querying":[36],"black-box":[38],"with":[40],"perturbed":[41],"inputs,":[42],"generating":[43],"graph":[45],"over":[46],"from":[48],"responses,":[50],"and":[51],"solving":[52],"partitioning":[54],"problem":[55],"to":[56,74],"select":[57],"most":[59],"relevant":[60],"components.":[61],"focus":[63],"general":[65],"approach":[66],"on":[67],"sequence-tosequence":[68],"problems,":[69],"adopting":[70],"variational":[72],"autoencoder":[73],"yield":[75],"meaningful":[76],"input":[77],"perturbations.":[78],"test":[80],"our":[81],"across":[83],"several":[84],"NLP":[85],"sequence":[86],"generation":[87],"tasks.":[88]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
