{"id":"https://openalex.org/W1986199072","doi":"https://doi.org/10.1017/s1351324908004907","title":"Does this list contain what you were searching for? Learning adaptive dialogue strategies for interactive question answering","display_name":"Does this list contain what you were searching for? Learning adaptive dialogue strategies for interactive question answering","publication_year":2008,"publication_date":"2008-10-22","ids":{"openalex":"https://openalex.org/W1986199072","doi":"https://doi.org/10.1017/s1351324908004907","mag":"1986199072"},"language":"en","primary_location":{"id":"doi:10.1017/s1351324908004907","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s1351324908004907","pdf_url":null,"source":{"id":"https://openalex.org/S18088403","display_name":"Natural Language Engineering","issn_l":"1351-3249","issn":["1351-3249","1469-8110"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Natural Language Engineering","raw_type":"journal-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/A5076593865","display_name":"Verena Rieser","orcid":"https://orcid.org/0000-0001-6117-4395"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"V. RIESER","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, Great Britain e-mail:","School of informatics, university of edinburgh, edinburgh, eh8 9ab, great britain e-mail: vrieser@inf.ed.ac.uk, olemon@inf.ed.ac.uk"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, Great Britain e-mail:","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of informatics, university of edinburgh, edinburgh, eh8 9ab, great britain e-mail: vrieser@inf.ed.ac.uk, olemon@inf.ed.ac.uk","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010949145","display_name":"Oliver Lemon","orcid":"https://orcid.org/0000-0001-9497-4743"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"O. LEMON","raw_affiliation_strings":["School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, Great Britain e-mail:","School of informatics, university of edinburgh, edinburgh, eh8 9ab, great britain e-mail: vrieser@inf.ed.ac.uk, olemon@inf.ed.ac.uk"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, Great Britain e-mail:","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"School of informatics, university of edinburgh, edinburgh, eh8 9ab, great britain e-mail: vrieser@inf.ed.ac.uk, olemon@inf.ed.ac.uk","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98677209"],"apc_list":null,"apc_paid":null,"fwci":7.0048,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.9644058,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"15","issue":"1","first_page":"55","last_page":"72"},"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/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.9962000250816345,"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":0.8656653165817261},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.671798050403595},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6537216901779175},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.649829089641571},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.45199280977249146},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.44087520241737366},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44058820605278015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36454999446868896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3245733976364136}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8656653165817261},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.671798050403595},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6537216901779175},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.649829089641571},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.45199280977249146},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.44087520241737366},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44058820605278015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36454999446868896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3245733976364136},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1017/s1351324908004907","is_oa":false,"landing_page_url":"https://doi.org/10.1017/s1351324908004907","pdf_url":null,"source":{"id":"https://openalex.org/S18088403","display_name":"Natural Language Engineering","issn_l":"1351-3249","issn":["1351-3249","1469-8110"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Natural Language Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.550000011920929,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G1904389866","display_name":null,"funder_award_id":"EP/E019501/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1375381","https://openalex.org/W62710299","https://openalex.org/W83775542","https://openalex.org/W97139432","https://openalex.org/W126125724","https://openalex.org/W154525918","https://openalex.org/W200223693","https://openalex.org/W263614502","https://openalex.org/W268459713","https://openalex.org/W392180792","https://openalex.org/W1485750786","https://openalex.org/W1515851193","https://openalex.org/W1542636192","https://openalex.org/W1681299129","https://openalex.org/W1695997110","https://openalex.org/W1767747848","https://openalex.org/W1945901314","https://openalex.org/W1962106326","https://openalex.org/W2031305583","https://openalex.org/W2066986772","https://openalex.org/W2080887205","https://openalex.org/W2083205357","https://openalex.org/W2096145771","https://openalex.org/W2101445408","https://openalex.org/W2106482783","https://openalex.org/W2111989294","https://openalex.org/W2112804987","https://openalex.org/W2132997613","https://openalex.org/W2134466368","https://openalex.org/W2141608262","https://openalex.org/W2319588593","https://openalex.org/W2791590751","https://openalex.org/W2911283634","https://openalex.org/W2914656440","https://openalex.org/W4301525937","https://openalex.org/W6638352555"],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2387743295","https://openalex.org/W2115758952","https://openalex.org/W3082787378","https://openalex.org/W2136007095","https://openalex.org/W2032233321","https://openalex.org/W2366230879","https://openalex.org/W3208425359","https://openalex.org/W2349927912","https://openalex.org/W3159777597"],"abstract_inverted_index":{"Abstract":[0],"Policy":[1],"learning":[2,30,115,163,221],"is":[3,121],"an":[4,97,177],"active":[5],"topic":[6],"in":[7,17,29,87,96,181],"dialogue":[8],"systems":[9],"research,":[10],"but":[11],"it":[12],"has":[13,216],"not":[14],"been":[15],"explored":[16],"relation":[18],"to":[19,44,54,56,61,103,111],"interactive":[20],"question":[21,35,41],"answering":[22,36],"(IQA).":[23],"We":[24,125],"take":[25],"a":[26,105,113,142,148,167,217],"first":[27],"step":[28],"adaptive":[31],"interaction":[32],"policies":[33,175,196],"for":[34,123,132,147],":":[37],"we":[38,101,128,209,223],"address":[39],"the":[40,57,65,69,72,75,78,80,85,88,186,192,194,201,212,228],"of":[42,71,77,82,144,151,157,170,183,191,214,227],"how":[43,50],"acquire":[45],"enough":[46],"reliable":[47],"query":[48],"constraints,":[49],"many":[51],"database":[52,83,215],"results":[53],"present":[55,62],"user":[58,154],"and":[59,84,109,159,222],"when":[60],"them,":[63],"given":[64],"competing":[66],"trade-offs":[67],"between":[68],"length":[70,76],"answer":[73],"list,":[74],"interaction,":[79],"type":[81,213],"noise":[86,152],"communication":[89],"channel.":[90],"The":[91,117,173],"operating":[92,171],"conditions":[93],"are":[94],"reflected":[95],"objective":[98,119],"function":[99,120],"which":[100,137],"use":[102],"derive":[104],"hand-coded":[106,145,187,202],"threshold-based":[107],"policy":[108,162],"rewards":[110],"train":[112],"reinforcement":[114],"policy.":[116],"same":[118],"used":[122],"evaluation.":[124],"show":[126,210],"that":[127,211],"can":[129],"learn":[130],"strategies":[131],"this":[133],"complex":[134],"trade-off":[135],"problem":[136],"perform":[138,197],"significantly":[139,198],"better":[140,199],"than":[141,200],"variety":[143],"policies,":[146],"wide":[149,168],"range":[150],"conditions,":[153],"types,":[155],"types":[156],"DB":[158],"turn-penalties.":[160],"Our":[161],"framework":[164],"thus":[165],"covers":[166],"spectrum":[169],"conditions.":[172],"learned":[174,195,229],"produce":[176],"average":[178],"relative":[179],"increase":[180],"reward":[182],"86.78%":[184],"over":[185],"policies.":[188,231],"In":[189],"93%":[190],"cases":[193],"ones":[203],"(":[204],"p":[205],"&lt;":[206],".001).":[207],"Furthermore":[208],"significant":[218],"effect":[219],"on":[220],"give":[224],"qualitative":[225],"descriptions":[226],"IQA":[230]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2016,"cited_by_count":1},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
