{"id":"https://openalex.org/W2097550833","doi":"https://doi.org/10.1109/slt.2010.5700816","title":"What is left to be understood in ATIS?","display_name":"What is left to be understood in ATIS?","publication_year":2010,"publication_date":"2010-12-01","ids":{"openalex":"https://openalex.org/W2097550833","doi":"https://doi.org/10.1109/slt.2010.5700816","mag":"2097550833"},"language":"en","primary_location":{"id":"doi:10.1109/slt.2010.5700816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2010.5700816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Spoken Language Technology Workshop","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/A5087941479","display_name":"G\u00f6khan T\u00fcr","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gokhan Tur","raw_affiliation_strings":["Microsoft Research Limited, Mountain View, CA, USA","Microsoft Research, Mountain View, CA, 94041, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Limited, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Mountain View, CA, 94041, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068709817","display_name":"Dilek Hakkani\u2010T\u00fcr","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dilek Hakkani-Tur","raw_affiliation_strings":["Microsoft Research Limited, Mountain View, CA, USA","Microsoft Research, Mountain View, CA, 94041, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Limited, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Mountain View, CA, 94041, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003679010","display_name":"Larry Heck","orcid":"https://orcid.org/0000-0003-3358-6362"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Larry Heck","raw_affiliation_strings":["Microsoft Research Limited, Mountain View, CA, USA","Microsoft Research, Mountain View, CA, 94041, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Limited, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1290206253"]},{"raw_affiliation_string":"Microsoft Research, Mountain View, CA, 94041, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087941479"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":7.0198,"has_fulltext":false,"cited_by_count":239,"citation_normalized_percentile":{"value":0.96817831,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","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/T12031","display_name":"Speech and dialogue systems","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":0.9998999834060669,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8490940928459167},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6943289637565613},{"id":"https://openalex.org/keywords/spoken-language","display_name":"Spoken language","score":0.5758011341094971},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5720537900924683},{"id":"https://openalex.org/keywords/natural-language-understanding","display_name":"Natural language understanding","score":0.5263354182243347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5161877274513245},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48685163259506226},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4868425726890564},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4841473698616028},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4646667242050171},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4273451566696167},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4224497377872467},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.41562506556510925},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4058094918727875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8490940928459167},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6943289637565613},{"id":"https://openalex.org/C2776230583","wikidata":"https://www.wikidata.org/wiki/Q1322198","display_name":"Spoken language","level":2,"score":0.5758011341094971},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5720537900924683},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.5263354182243347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5161877274513245},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48685163259506226},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4868425726890564},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4841473698616028},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4646667242050171},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4273451566696167},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4224497377872467},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.41562506556510925},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4058094918727875},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/slt.2010.5700816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2010.5700816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Spoken Language Technology Workshop","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W21503661","https://openalex.org/W31405998","https://openalex.org/W178897730","https://openalex.org/W1550863320","https://openalex.org/W1676280465","https://openalex.org/W1936920915","https://openalex.org/W2037595448","https://openalex.org/W2053463056","https://openalex.org/W2082761062","https://openalex.org/W2091671846","https://openalex.org/W2093973850","https://openalex.org/W2096435848","https://openalex.org/W2098880560","https://openalex.org/W2105992791","https://openalex.org/W2124246394","https://openalex.org/W2129554061","https://openalex.org/W2140279531","https://openalex.org/W2147880316","https://openalex.org/W2148603752","https://openalex.org/W2153501885","https://openalex.org/W2155858122","https://openalex.org/W2160042006","https://openalex.org/W2162455891","https://openalex.org/W2166293310","https://openalex.org/W2171144711","https://openalex.org/W6601312511","https://openalex.org/W6640440542","https://openalex.org/W6680672469","https://openalex.org/W6682082992","https://openalex.org/W6683366680","https://openalex.org/W6684017090"],"related_works":["https://openalex.org/W3174008653","https://openalex.org/W2962716343","https://openalex.org/W2765804957","https://openalex.org/W2145230572","https://openalex.org/W4288099861","https://openalex.org/W2893411096","https://openalex.org/W4224919006","https://openalex.org/W43702919","https://openalex.org/W4213400064","https://openalex.org/W4288263119"],"abstract_inverted_index":{"One":[0],"of":[1,55,89,172],"the":[2,11,26,64,86,113],"main":[3],"data":[4,158,191],"resources":[5],"used":[6],"in":[7,21,36,170,181],"many":[8,143],"studies":[9,47],"over":[10],"past":[12],"two":[13],"decades":[14],"for":[15,53],"spoken":[16,22,190],"language":[17],"understanding":[18],"(SLU)":[19],"research":[20,93],"dialog":[23],"systems":[24],"is":[25,78,100],"airline":[27],"travel":[28],"information":[29],"system":[30],"(ATIS)":[31],"corpus.":[32,94],"Two":[33],"primary":[34],"tasks":[35,57,163],"SLU":[37,115,182],"are":[38],"intent":[39],"determination":[40],"(ID)":[41],"and":[42,147,174,193,207],"slot":[43],"filling":[44],"(SF).":[45],"Recent":[46],"reported":[48],"error":[49,71,127,136],"rates":[50,72],"below":[51],"5%":[52],"both":[54],"these":[56,69],"employing":[58,194],"discriminative":[59],"machine":[60],"learning":[61],"techniques":[62,106],"with":[63,103,112,133,164],"ATIS":[65,90,138],"test":[66,139],"set.":[67],"While":[68],"low":[70,135],"may":[73],"suggest":[74],"that":[75,131,179],"this":[76,96,123],"task":[77],"close":[79],"to":[80,121,203,210],"being":[81],"solved,":[82],"further":[83],"analysis":[84],"reveals":[85],"continued":[87],"utility":[88,124],"as":[91],"a":[92],"In":[95],"paper,":[97],"our":[98],"goal":[99],"not":[101],"experimenting":[102],"domain":[104],"specific":[105],"or":[107],"features":[108,198],"which":[109],"can":[110,167,183],"help":[111],"remaining":[114],"errors,":[116],"but":[117],"instead":[118],"exploring":[119],"methods":[120],"realize":[122],"via":[125],"extensive":[126],"analysis.":[128],"We":[129,177],"conclude":[130],"even":[132],"such":[134],"rates,":[137],"set":[140],"still":[141],"includes":[142],"unseen":[144],"example":[145],"categories":[146],"sequences,":[148],"hence":[149],"requires":[150],"more":[151,161,188,195],"data.":[152],"Better":[153],"yet,":[154],"new":[155],"annotated":[156],"larger":[157],"sets":[159,192],"from":[160],"complex":[162],"realistic":[165],"utterances":[166],"avoid":[168],"over-tuning":[169],"terms":[171],"modeling":[173],"feature":[175],"design.":[176],"believe":[178],"advancements":[180],"be":[184],"achieved":[185],"by":[186],"having":[187],"naturally":[189],"linguistically":[196],"motivated":[197],"while":[199],"preserving":[200],"robustness":[201],"due":[202,209],"speech":[204],"recognition":[205],"noise":[206],"variance":[208],"natural":[211],"language.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":26},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":29},{"year":2020,"cited_by_count":46},{"year":2019,"cited_by_count":30},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":13},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
