{"id":"https://openalex.org/W2587528408","doi":"https://doi.org/10.18653/v1/p17-2081","title":"Question Answering through Transfer Learning from Large Fine-grained Supervision Data","display_name":"Question Answering through Transfer Learning from Large Fine-grained Supervision Data","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2587528408","doi":"https://doi.org/10.18653/v1/p17-2081","mag":"2587528408"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p17-2081","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2081","pdf_url":"https://www.aclweb.org/anthology/P17-2081.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P17-2081.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039158419","display_name":"Sewon Min","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"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":["KR","US"],"is_corresponding":true,"raw_author_name":"Sewon Min","raw_affiliation_strings":["University of Washington","Seoul National University"],"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104331009","display_name":"Minjoon Seo","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"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":["KR","US"],"is_corresponding":false,"raw_author_name":"Minjoon Seo","raw_affiliation_strings":["University of Washington","Seoul National University"],"affiliations":[{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082305994","display_name":"Hannaneh Hajishirzi","orcid":"https://orcid.org/0000-0002-1055-6657"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"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":["KR","US"],"is_corresponding":false,"raw_author_name":"Hannaneh Hajishirzi","raw_affiliation_strings":["Seoul National University","University of Washington"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"University of Washington","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5039158419"],"corresponding_institution_ids":["https://openalex.org/I139264467","https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":17.2525,"has_fulltext":true,"cited_by_count":114,"citation_normalized_percentile":{"value":0.99293124,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"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/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9976999759674072,"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":0.8542671799659729},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.842840313911438},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7353119850158691},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7331968545913696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6389934420585632},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6226972937583923},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.5045286417007446},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41615304350852966},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34191203117370605},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08990365266799927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8542671799659729},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.842840313911438},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7353119850158691},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7331968545913696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6389934420585632},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6226972937583923},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.5045286417007446},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41615304350852966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34191203117370605},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08990365266799927},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p17-2081","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2081","pdf_url":"https://www.aclweb.org/anthology/P17-2081.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p17-2081","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-2081","pdf_url":"https://www.aclweb.org/anthology/P17-2081.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G1356082100","display_name":null,"funder_award_id":"IIS 1616112","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4325372433","display_name":"RI: Small: Learning to Read, Ground, and Reason in Multimodal Text","funder_award_id":"1616112","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320317052","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2587528408.pdf","grobid_xml":"https://content.openalex.org/works/W2587528408.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W70399244","https://openalex.org/W98423455","https://openalex.org/W1544827683","https://openalex.org/W1614298861","https://openalex.org/W1840435438","https://openalex.org/W1849277567","https://openalex.org/W2028175314","https://openalex.org/W2106612368","https://openalex.org/W2108598243","https://openalex.org/W2111854888","https://openalex.org/W2131494463","https://openalex.org/W2153579005","https://openalex.org/W2211192759","https://openalex.org/W2250307601","https://openalex.org/W2250539671","https://openalex.org/W2250790822","https://openalex.org/W2251818205","https://openalex.org/W2251919380","https://openalex.org/W2252136820","https://openalex.org/W2310102669","https://openalex.org/W2464096189","https://openalex.org/W2467646401","https://openalex.org/W2468484304","https://openalex.org/W2468672598","https://openalex.org/W2471915251","https://openalex.org/W2481240925","https://openalex.org/W2516930406","https://openalex.org/W2551396370","https://openalex.org/W2552027021","https://openalex.org/W2557764419","https://openalex.org/W2558203065","https://openalex.org/W2593833795","https://openalex.org/W2606004785","https://openalex.org/W2915240437","https://openalex.org/W2949615363","https://openalex.org/W2951528484","https://openalex.org/W2951534261","https://openalex.org/W2963080779","https://openalex.org/W2963448850","https://openalex.org/W2963748441","https://openalex.org/W2963871484","https://openalex.org/W2964091467","https://openalex.org/W2964352358","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2366230879","https://openalex.org/W3208425359","https://openalex.org/W2349927912","https://openalex.org/W4288102755","https://openalex.org/W2103247590","https://openalex.org/W3159777597"],"abstract_inverted_index":{"We":[0,26,64,87],"show":[1,89],"that":[2,66,90],"the":[3,13,28,31,56,97,100],"task":[4],"of":[5,16,30,99],"question":[6],"answering":[7],"(QA)":[8],"can":[9],"significantly":[10],"benefit":[11],"from":[12,49],"transfer":[14,46,93],"learning":[15,47,73,94],"models":[17],"trained":[18],"on":[19,102],"a":[20,44,91],"different":[21],"large,":[22],"fine-grained":[23],"QA":[24,36],"dataset.":[25],"achieve":[27],"state":[29,98],"art":[32,101],"in":[33],"two":[34],"well-studied":[35],"datasets,":[37],"WikiQA":[38],"and":[39,75,84],"SemEval-2016":[40],"(Task":[41],"3A),":[42],"through":[43,81],"basic":[45],"technique":[48],"SQuAD.":[50],"For":[51],"WikiQA,":[52],"our":[53],"model":[54,59],"outperforms":[55],"previous":[57],"best":[58],"by":[60],"more":[61],"than":[62,78],"8%.":[63],"demonstrate":[65],"finer":[67],"supervision":[68],"provides":[69],"better":[70],"guidance":[71],"for":[72],"lexical":[74],"syntactic":[76],"information":[77],"coarser":[79],"supervision,":[80],"quantitative":[82],"results":[83],"visual":[85],"analysis.":[86],"also":[88],"similar":[92],"procedure":[95],"achieves":[96],"an":[103],"entailment":[104],"task.":[105]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":48},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}