{"id":"https://openalex.org/W2950561955","doi":"https://doi.org/10.18653/v1/p19-1556","title":"Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks","display_name":"Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2950561955","doi":"https://doi.org/10.18653/v1/p19-1556","mag":"2950561955"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1556","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1556","pdf_url":"https://www.aclweb.org/anthology/P19-1556.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":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1556.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084805216","display_name":"Anush Kamath","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anush Kamath","raw_affiliation_strings":["Carnegie Mellon University Pittsburgh, PA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University Pittsburgh, PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087185904","display_name":"Sparsh Gupta","orcid":"https://orcid.org/0000-0002-3655-5767"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sparsh Gupta","raw_affiliation_strings":["University of California San Diego San Diego, CA"],"affiliations":[{"raw_affiliation_string":"University of California San Diego San Diego, CA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071982498","display_name":"V\u0131\u0301tor Carvalho","orcid":"https://orcid.org/0000-0003-4658-5844"},"institutions":[{"id":"https://openalex.org/I88773910","display_name":"Intuit (United States)","ror":"https://ror.org/049mrbr98","country_code":"US","type":"company","lineage":["https://openalex.org/I88773910"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vitor Carvalho","raw_affiliation_strings":["Intuit AI San Diego, CA"],"affiliations":[{"raw_affiliation_string":"Intuit AI San Diego, CA","institution_ids":["https://openalex.org/I88773910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084805216"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.6802,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.88212979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5545","last_page":"5550"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9975000023841858,"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":0.9975000023841858,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9908999800682068,"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":0.9330999851226807,"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/adversarial-system","display_name":"Adversarial system","score":0.8613126277923584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8227358460426331},{"id":"https://openalex.org/keywords/reversing","display_name":"Reversing","score":0.7617980241775513},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.7614848017692566},{"id":"https://openalex.org/keywords/data-deduplication","display_name":"Data deduplication","score":0.7301139831542969},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.7228999137878418},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6517285108566284},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6399722695350647},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5556907057762146},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33711233735084534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07698392868041992},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07201266288757324}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8613126277923584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8227358460426331},{"id":"https://openalex.org/C2781085045","wikidata":"https://www.wikidata.org/wiki/Q7318308","display_name":"Reversing","level":2,"score":0.7617980241775513},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.7614848017692566},{"id":"https://openalex.org/C32587265","wikidata":"https://www.wikidata.org/wiki/Q1182260","display_name":"Data deduplication","level":2,"score":0.7301139831542969},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.7228999137878418},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6517285108566284},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6399722695350647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5556907057762146},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33711233735084534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07698392868041992},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07201266288757324},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-1556","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1556","pdf_url":"https://www.aclweb.org/anthology/P19-1556.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"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1556","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1556","pdf_url":"https://www.aclweb.org/anthology/P19-1556.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","id":"https://metadata.un.org/sdg/4","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2950561955.pdf","grobid_xml":"https://content.openalex.org/works/W2950561955.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1882958252","https://openalex.org/W2130158090","https://openalex.org/W2256784804","https://openalex.org/W2511131004","https://openalex.org/W2551835155","https://openalex.org/W2579563823","https://openalex.org/W2593768305","https://openalex.org/W2593833795","https://openalex.org/W2605488490","https://openalex.org/W2773963565","https://openalex.org/W2788305286","https://openalex.org/W2798469938","https://openalex.org/W2798984401","https://openalex.org/W2888161220","https://openalex.org/W2892202790","https://openalex.org/W2906139657","https://openalex.org/W2949523712","https://openalex.org/W2953127297","https://openalex.org/W2962992635","https://openalex.org/W2963540468","https://openalex.org/W2963826681","https://openalex.org/W2964278684","https://openalex.org/W3188313643"],"related_works":["https://openalex.org/W2354780065","https://openalex.org/W3011985225","https://openalex.org/W2482155543","https://openalex.org/W2475618665","https://openalex.org/W4224280204","https://openalex.org/W2380977331","https://openalex.org/W2373606770","https://openalex.org/W1995189325","https://openalex.org/W2885769062","https://openalex.org/W2374738829"],"abstract_inverted_index":{"Adversarial":[0],"domain":[1,29,56,61,68],"adaptation":[2,30,76],"has":[3],"been":[4],"recently":[5],"introduced":[6],"as":[7,16,91,93],"an":[8],"effective":[9],"technique":[10],"for":[11,55,74],"textual":[12,43,96],"matching":[13],"tasks,":[14,99],"such":[15],"question":[17,89],"deduplication":[18,90],"Here":[19],"we":[20],"investigate":[21],"the":[22],"use":[23],"of":[24],"gradient":[25,48],"reversal":[26,49],"on":[27,77,88,94,110],"adversarial":[28,75],"to":[31,102],"explicitly":[32,53],"learn":[33],"both":[34],"shared":[35],"and":[36,80],"unshared":[37],"(domain":[38],"specific)":[39],"representations":[40],"between":[41],"two":[42],"domains.":[44],"In":[45],"doing":[46],"so,":[47],"learns":[50],"features":[51],"that":[52,64,82],"compensate":[54],"mismatch,":[57],"while":[58],"still":[59],"distilling":[60],"specific":[62],"knowledge":[63],"can":[65],"improve":[66],"target":[67],"accuracy.":[69],"We":[70],"evaluate":[71],"reversing":[72],"gradients":[73],"multiple":[78],"domains,":[79],"demonstrate":[81],"it":[83],"significantly":[84],"outperforms":[85],"other":[86],"methods":[87],"well":[92],"recognizing":[95],"entailment":[97],"(RTE)":[98],"achieving":[100],"up":[101],"7%":[103],"absolute":[104],"boost":[105],"in":[106],"base":[107],"model":[108],"accuracy":[109],"some":[111],"datasets.":[112]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}