{"id":"https://openalex.org/W2741998045","doi":"https://doi.org/10.1145/3077136.3084147","title":"LSTM vs. BM25 for Open-domain QA","display_name":"LSTM vs. BM25 for Open-domain QA","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2741998045","doi":"https://doi.org/10.1145/3077136.3084147","mag":"2741998045"},"language":"en","primary_location":{"id":"doi:10.1145/3077136.3084147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3084147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020921043","display_name":"Sosuke Kato","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sosuke Kato","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006505274","display_name":"Riku Togashi","orcid":"https://orcid.org/0000-0001-9026-0495"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riku Togashi","raw_affiliation_strings":["Yahoo Japan Corporation, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Japan Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000008405","display_name":"Hideyuki Maeda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hideyuki Maeda","raw_affiliation_strings":["Yahoo Japan Corporation, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Japan Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082125155","display_name":"Sumio Fujita","orcid":"https://orcid.org/0000-0002-1282-386X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sumio Fujita","raw_affiliation_strings":["Yahoo Japan Corporation, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Japan Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023595778","display_name":"Tetsuya Sakai","orcid":"https://orcid.org/0000-0002-6720-963X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Sakai","raw_affiliation_strings":["Waseda University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1994,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62629552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1309","last_page":"1312"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.989300012588501,"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":0.8938884139060974},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.8765755891799927},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.8080825805664062},{"id":"https://openalex.org/keywords/open-domain","display_name":"Open domain","score":0.7796083688735962},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6162725687026978},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6009881496429443},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5404220223426819},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.5368993282318115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5249360203742981},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5062538981437683},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.41124963760375977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8938884139060974},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.8765755891799927},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8080825805664062},{"id":"https://openalex.org/C2993776861","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Open domain","level":3,"score":0.7796083688735962},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6162725687026978},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6009881496429443},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5404220223426819},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.5368993282318115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5249360203742981},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5062538981437683},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.41124963760375977},{"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3077136.3084147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3077136.3084147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1218873234","display_name":"Exploratory Search Considering the User's Situation","funder_award_id":"16H01756","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W113194788","https://openalex.org/W1482214997","https://openalex.org/W2019432678","https://openalex.org/W2251202616","https://openalex.org/W2402804674","https://openalex.org/W2517782820","https://openalex.org/W2572666432","https://openalex.org/W2575773175","https://openalex.org/W2593172760","https://openalex.org/W2916259791"],"related_works":["https://openalex.org/W2951097643","https://openalex.org/W2391533720","https://openalex.org/W4309395021","https://openalex.org/W4307481286","https://openalex.org/W3215363805","https://openalex.org/W2898767136","https://openalex.org/W204133468","https://openalex.org/W2991310128","https://openalex.org/W2395174199","https://openalex.org/W4226441484"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"neural":[3],"networks,":[4],"along":[5],"with":[6,125],"the":[7,60,77,99,144,152,179,187,191],"growth":[8],"of":[9,73,101,111,167],"rich":[10],"and":[11,136,143,185],"diverse":[12],"community":[13],"question":[14,25],"answering":[15,26],"(cQA)":[16],"data,":[17],"have":[18],"enabled":[19],"researchers":[20],"to":[21,106],"construct":[22],"robust":[23],"open-domain":[24,113],"(QA)":[27],"systems.":[28],"It":[29],"is":[30,164],"often":[31],"claimed":[32],"that":[33],"such":[34,43,48,84],"state-of-the-art":[35],"QA":[36,63,114,170],"systems":[37,150,193],"far":[38],"outperform":[39],"traditional":[40],"IR":[41],"baselines":[42],"as":[44,76],"BM25.":[45,148],"However,":[46],"most":[47],"studies":[49],"rely":[50],"on":[51,118,147],"relatively":[52],"small":[53],"data":[54,68],"sets,":[55],"e.g.,":[56],"those":[57],"extracted":[58],"from":[59,190],"old":[61],"TREC":[62],"tracks.":[64],"Given":[65],"massive":[66],"training":[67],"plus":[69],"a":[70,85,108,119,165],"separate":[71],"corpus":[72],"Q&A":[74,154],"pairs":[75],"target":[78],"knowledge":[79,155],"source,":[80],"how":[81],"well":[82],"would":[83,91],"system":[86,163],"really":[87],"perform?":[88],"How":[89],"fast":[90],"it":[92],"respond?":[93],"In":[94],"this":[95],"demonstration,":[96],"we":[97,173],"provide":[98],"attendees":[100,181],"SIGIR":[102,180],"2017":[103],"an":[104],"opportunity":[105],"experience":[107],"live":[109],"comparison":[110],"two":[112,192],"systems,":[115,171],"one":[116],"based":[117,146],"long":[120],"short-term":[121],"memory":[122],"(LSTM)":[123],"architecture":[124],"over":[126,137],"11":[127],"million":[128,139],"Yahoo!":[129,133],"Chiebukuro":[130],"(i.e.,":[131],"Japanese":[132,168,188],"Answers)":[134],"questions":[135,184],"27.4":[138],"answers":[140],"for":[141,157,177],"training,":[142],"other":[145],"Both":[149],"use":[151],"same":[153],"source":[156],"answer":[158],"retrieval.":[159],"Our":[160],"core":[161],"demonstration":[162],"pair":[166],"monolingual":[169],"but":[172],"leverage":[174],"machine":[175],"translation":[176],"letting":[178],"enter":[182],"English":[183],"compare":[186],"responses":[189],"after":[194],"translating":[195],"them":[196],"into":[197],"English.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}