{"id":"https://openalex.org/W2890894339","doi":"https://doi.org/10.18653/v1/d18-1260","title":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","display_name":"Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2890894339","doi":"https://doi.org/10.18653/v1/d18-1260","mag":"2890894339"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1260","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1260","pdf_url":"https://www.aclweb.org/anthology/D18-1260.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1260.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052408504","display_name":"Todor Mihaylov","orcid":null},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Todor Mihaylov","raw_affiliation_strings":["Research Training Group AIPHES & Heidelberg University, Heidelberg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Training Group AIPHES & Heidelberg University, Heidelberg, Germany","institution_ids":["https://openalex.org/I223822909"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081635691","display_name":"Peter E. Clark","orcid":"https://orcid.org/0000-0002-8006-7015"},"institutions":[{"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":"Peter Clark","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A","institution_ids":["https://openalex.org/I4210156221"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033257344","display_name":"Tushar Khot","orcid":null},"institutions":[{"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":"Tushar Khot","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A","institution_ids":["https://openalex.org/I4210156221"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077726785","display_name":"Ashish Sabharwal","orcid":null},"institutions":[{"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":"Ashish Sabharwal","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA, U.S.A","institution_ids":["https://openalex.org/I4210156221"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":24.6729,"has_fulltext":true,"cited_by_count":658,"citation_normalized_percentile":{"value":0.99533402,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2381","last_page":"2391"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9965000152587891,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9832000136375427,"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":0.7668864727020264},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.7624447345733643},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6059393286705017},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.601162850856781},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5517105460166931},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49047109484672546},{"id":"https://openalex.org/keywords/subject","display_name":"Subject (documents)","score":0.4527478814125061},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4093485474586487},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.33238404989242554},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.2664198875427246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7668864727020264},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.7624447345733643},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6059393286705017},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.601162850856781},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5517105460166931},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49047109484672546},{"id":"https://openalex.org/C2777855551","wikidata":"https://www.wikidata.org/wiki/Q12310021","display_name":"Subject (documents)","level":2,"score":0.4527478814125061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4093485474586487},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33238404989242554},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2664198875427246},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1260","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1260","pdf_url":"https://www.aclweb.org/anthology/D18-1260.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1260","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1260","pdf_url":"https://www.aclweb.org/anthology/D18-1260.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2890894339.pdf","grobid_xml":"https://content.openalex.org/works/W2890894339.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1493490255","https://openalex.org/W1522301498","https://openalex.org/W1544827683","https://openalex.org/W2081580037","https://openalex.org/W2101234009","https://openalex.org/W2103012681","https://openalex.org/W2107901333","https://openalex.org/W2125436846","https://openalex.org/W2126209950","https://openalex.org/W2127978399","https://openalex.org/W2250539671","https://openalex.org/W2337282450","https://openalex.org/W2341790067","https://openalex.org/W2467646401","https://openalex.org/W2468484304","https://openalex.org/W2512735275","https://openalex.org/W2547185913","https://openalex.org/W2557764419","https://openalex.org/W2561529111","https://openalex.org/W2575101981","https://openalex.org/W2604832008","https://openalex.org/W2605137890","https://openalex.org/W2606818403","https://openalex.org/W2608309533","https://openalex.org/W2608787653","https://openalex.org/W2626154462","https://openalex.org/W2746097825","https://openalex.org/W2758592353","https://openalex.org/W2788496822","https://openalex.org/W2794325560","https://openalex.org/W2804897457","https://openalex.org/W2805387248","https://openalex.org/W2899771611","https://openalex.org/W2915240437","https://openalex.org/W2949615363","https://openalex.org/W2962736243","https://openalex.org/W2962739339","https://openalex.org/W2962809918","https://openalex.org/W2962985038","https://openalex.org/W2963318894","https://openalex.org/W2963339397","https://openalex.org/W2963681467","https://openalex.org/W2963691697","https://openalex.org/W2963748441","https://openalex.org/W2963829073","https://openalex.org/W2963866616","https://openalex.org/W2963918774","https://openalex.org/W2963963993","https://openalex.org/W2963983586","https://openalex.org/W2964121744","https://openalex.org/W2964267515","https://openalex.org/W2964302598","https://openalex.org/W4233413206","https://openalex.org/W4244808249","https://openalex.org/W4298110152","https://openalex.org/W4298149550","https://openalex.org/W4386506836","https://openalex.org/W6600757733"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2474469336","https://openalex.org/W2391431582","https://openalex.org/W2805599431","https://openalex.org/W2357461155","https://openalex.org/W2374451721","https://openalex.org/W2384129116","https://openalex.org/W2354251581","https://openalex.org/W3145924829","https://openalex.org/W2353647904"],"abstract_inverted_index":{"We":[0,164],"present":[1],"a":[2,20,31,70,100,168],"new":[3],"kind":[4],"of":[5,19,33,45,72,76,103,109,156],"question":[6],"answering":[7],"dataset,":[8],"OpenBookQA,":[9],"modeled":[10],"after":[11],"open":[12,23,58,159],"book":[13,24,59,160],"exams":[14],"for":[15],"assessing":[16],"human":[17,186],"understanding":[18,44,102],"subject.":[21],"The":[22],"that":[25],"comes":[26],"with":[27,65],"our":[28],"questions":[29,41],"is":[30,74,115,122],"set":[32],"1326":[34],"elementary":[35],"level":[36],"science":[37],"facts.":[38,163],"Roughly":[39],"6000":[40],"probe":[42],"an":[43,57],"these":[46],"facts":[47],"and":[48,161,179],"their":[49],"application":[50],"to":[51,124,147,170,180,185],"novel":[52],"situations.":[53],"This":[54],"requires":[55],"combining":[56],"fact":[60],"(e.g.,":[61,69],"metals":[62],"conduct":[63],"electricity)":[64],"broad":[66],"common":[67,110],"knowledge":[68,89,150],"suit":[71],"armor":[73],"made":[75],"metal)":[77],"obtained":[78],"from":[79],"other":[80],"sources.":[81],"While":[82],"existing":[83],"QA":[84,130],"datasets":[85],"over":[86],"documents":[87],"or":[88],"bases,":[90],"being":[91],"generally":[92],"self-contained,":[93],"focus":[94],"on":[95,120],"linguistic":[96],"understanding,":[97],"OpenBookQA":[98,121],"probes":[99],"deeper":[101],"both":[104,157],"the":[105,107,112,149,154,158,172,182],"topic-in":[106],"context":[108],"knowledge-and":[111],"language":[113],"it":[114,166],"expressed":[116],"in.":[117],"Human":[118],"performance":[119],"close":[123,181],"92%,":[125],"but":[126],"many":[127],"state-of-the-art":[128],"pre-trained":[129],"methods":[131],"perform":[132],"surprisingly":[133],"poorly,":[134],"worse":[135],"than":[136],"several":[137],"simple":[138],"neural":[139],"baselines":[140],"we":[141],"develop.":[142],"Our":[143],"oracle":[144],"experiments":[145],"designed":[146],"circumvent":[148],"retrieval":[151,173],"bottleneck":[152],"demonstrate":[153],"value":[155],"additional":[162],"leave":[165],"as":[167],"challenge":[169],"solve":[171],"problem":[174],"in":[175],"this":[176],"multi-hop":[177],"setting":[178],"large":[183],"gap":[184],"performance.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":73},{"year":2025,"cited_by_count":110},{"year":2024,"cited_by_count":88},{"year":2023,"cited_by_count":105},{"year":2022,"cited_by_count":64},{"year":2021,"cited_by_count":81},{"year":2020,"cited_by_count":76},{"year":2019,"cited_by_count":56},{"year":2018,"cited_by_count":5}],"updated_date":"2026-08-02T14:50:37.381335","created_date":"2025-10-10T00:00:00"}