{"id":"https://openalex.org/W2981073299","doi":"https://doi.org/10.18653/v1/d19-5826","title":"Domain-agnostic Question-Answering with Adversarial Training","display_name":"Domain-agnostic Question-Answering with Adversarial Training","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2981073299","doi":"https://doi.org/10.18653/v1/d19-5826","mag":"2981073299"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d19-5826","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5826","pdf_url":"https://www.aclweb.org/anthology/D19-5826.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 2nd Workshop on Machine Reading for Question Answering","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/D19-5826.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070831085","display_name":"Seanie Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Seanie Lee","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113116672","display_name":"Donggyu Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Donggyu Kim","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080512264","display_name":"Jangwon Park","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Jangwon Park","raw_affiliation_strings":["Samsung Research, Seoul, Korea","[Samsung]"],"affiliations":[{"raw_affiliation_string":"Samsung Research, Seoul, Korea","institution_ids":["https://openalex.org/I2250650973"]},{"raw_affiliation_string":"[Samsung]","institution_ids":["https://openalex.org/I4210101778"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5070831085"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4336,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.72527388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"196","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9980999827384949,"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":0.995199978351593,"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":0.8746820688247681},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.8459845781326294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7831898927688599},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7206021547317505},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7047363519668579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6818775534629822},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6807385087013245},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5859151482582092},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5746605396270752},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4566965699195862},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41970300674438477},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37391334772109985},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06843879818916321},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05786573886871338}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8746820688247681},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8459845781326294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7831898927688599},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7206021547317505},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7047363519668579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6818775534629822},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6807385087013245},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5859151482582092},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5746605396270752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4566965699195862},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41970300674438477},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37391334772109985},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06843879818916321},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05786573886871338},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/d19-5826","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5826","pdf_url":"https://www.aclweb.org/anthology/D19-5826.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 2nd Workshop on Machine Reading for Question Answering","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1910.09342","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.09342","pdf_url":"https://arxiv.org/pdf/1910.09342","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:2981073299","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1910.09342","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.1910.09342","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1910.09342","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.18653/v1/d19-5826","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-5826","pdf_url":"https://www.aclweb.org/anthology/D19-5826.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 2nd Workshop on Machine Reading for Question Answering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2981073299.pdf","grobid_xml":"https://content.openalex.org/works/W2981073299.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W96659543","https://openalex.org/W1731081199","https://openalex.org/W1852255964","https://openalex.org/W1920962657","https://openalex.org/W1981233333","https://openalex.org/W2051167396","https://openalex.org/W2099471712","https://openalex.org/W2125436846","https://openalex.org/W2155858138","https://openalex.org/W2250864761","https://openalex.org/W2427527485","https://openalex.org/W2523586319","https://openalex.org/W2604763608","https://openalex.org/W2609826708","https://openalex.org/W2612431505","https://openalex.org/W2624677889","https://openalex.org/W2740132093","https://openalex.org/W2746097825","https://openalex.org/W2760600531","https://openalex.org/W2763549966","https://openalex.org/W2771039833","https://openalex.org/W2786606199","https://openalex.org/W2787560479","https://openalex.org/W2793978524","https://openalex.org/W2794557536","https://openalex.org/W2798658180","https://openalex.org/W2889965839","https://openalex.org/W2899771611","https://openalex.org/W2911681509","https://openalex.org/W2912924812","https://openalex.org/W2949776890","https://openalex.org/W2950501607","https://openalex.org/W2951365061","https://openalex.org/W2952848538","https://openalex.org/W2952862139","https://openalex.org/W2962897020","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963424663","https://openalex.org/W2963729324","https://openalex.org/W2964120615","https://openalex.org/W2964268978"],"related_works":["https://openalex.org/W2984402309","https://openalex.org/W2963597678","https://openalex.org/W3150743613","https://openalex.org/W2771669035","https://openalex.org/W3016869290","https://openalex.org/W2581637843","https://openalex.org/W2952746403","https://openalex.org/W3093475218","https://openalex.org/W2786472963","https://openalex.org/W3174891612","https://openalex.org/W3039577736","https://openalex.org/W3174318528","https://openalex.org/W3023958515","https://openalex.org/W2950197980","https://openalex.org/W2798384789","https://openalex.org/W2949464580","https://openalex.org/W2586087033","https://openalex.org/W2770653590","https://openalex.org/W2984284833","https://openalex.org/W3102032239"],"abstract_inverted_index":{"Adapting":[0],"models":[1,53],"to":[2,78],"new":[3],"domain":[4,24],"without":[5],"finetuning":[6],"is":[7,44],"a":[8,35,40],"challenging":[9],"problem":[10],"in":[11,26,46,68],"deep":[12],"learning.":[13],"In":[14],"this":[15,66],"paper,":[16],"we":[17],"utilize":[18],"an":[19],"adversarial":[20,48],"training":[21,43],"framework":[22],"for":[23],"generalization":[25],"Question":[27],"Answering":[28],"(QA)":[29],"task.":[30],"Our":[31],"model":[32,38,59],"consists":[33],"of":[34],"conventional":[36],"QA":[37,58],"and":[39,73],"discriminator.":[41],"The":[42],"performed":[45],"the":[47,51,79],"manner,":[49],"where":[50],"two":[52],"constantly":[54],"compete,":[55],"so":[56],"that":[57],"can":[60],"learn":[61],"domain-invariant":[62],"features.":[63],"We":[64],"apply":[65],"approach":[67],"MRQA":[69],"Shared":[70],"Task":[71],"2019":[72],"show":[74],"better":[75],"performance":[76],"compared":[77],"baseline":[80],"model.":[81]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}