{"id":"https://openalex.org/W4321109541","doi":"https://doi.org/10.1145/3584701","title":"Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation","display_name":"Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation","publication_year":2023,"publication_date":"2023-02-16","ids":{"openalex":"https://openalex.org/W4321109541","doi":"https://doi.org/10.1145/3584701"},"language":"en","primary_location":{"id":"doi:10.1145/3584701","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584701","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584701","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3584701","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013569210","display_name":"Jiazhan Feng","orcid":"https://orcid.org/0000-0002-5832-6199"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiazhan Feng","raw_affiliation_strings":["Wangxuan Institute of Computer Technology and School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5832-6199","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology and School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073065834","display_name":"Chongyang Tao","orcid":"https://orcid.org/0000-0002-4162-2119"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongyang Tao","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4162-2119","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103044463","display_name":"Xueliang Zhao","orcid":"https://orcid.org/0000-0001-9418-8881"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueliang Zhao","raw_affiliation_strings":["Wangxuan Institute of Computer Technology and Center for Data Science, AAIS, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9418-8881","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology and Center for Data Science, AAIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037132097","display_name":"Dongyan Zhao","orcid":"https://orcid.org/0000-0002-0396-6703"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyan Zhao","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University; National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China","Wangxuan Institute of Computer Technology, Peking University"],"raw_orcid":"https://orcid.org/0000-0002-0396-6703","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University; National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210100255"]},{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8331,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.87531223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"41","issue":"4","first_page":"1","last_page":"27"},"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/T12031","display_name":"Speech and dialogue systems","score":0.9980999827384949,"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":0.9944000244140625,"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":0.8437415361404419},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6344802975654602},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5812262296676636},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5731140971183777},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5311803817749023},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5094690918922424},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.478315144777298},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4578075110912323},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4525229334831238},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.446682333946228},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4399348497390747},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.42523249983787537}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8437415361404419},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6344802975654602},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5812262296676636},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5731140971183777},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5311803817749023},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5094690918922424},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.478315144777298},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4578075110912323},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4525229334831238},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.446682333946228},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4399348497390747},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.42523249983787537},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3584701","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584701","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584701","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3584701","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3584701","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3584701","source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4321109541.pdf","grobid_xml":"https://content.openalex.org/works/W4321109541.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1502957213","https://openalex.org/W1516184288","https://openalex.org/W1593271688","https://openalex.org/W1602136775","https://openalex.org/W2101105183","https://openalex.org/W2161466446","https://openalex.org/W2339852062","https://openalex.org/W2531563875","https://openalex.org/W2581637843","https://openalex.org/W2593581739","https://openalex.org/W2597601064","https://openalex.org/W2599674900","https://openalex.org/W2607303097","https://openalex.org/W2750779823","https://openalex.org/W2781528640","https://openalex.org/W2783215745","https://openalex.org/W2798456655","https://openalex.org/W2887428522","https://openalex.org/W2890394457","https://openalex.org/W2891416139","https://openalex.org/W2891826200","https://openalex.org/W2908331278","https://openalex.org/W2912512851","https://openalex.org/W2913106281","https://openalex.org/W2915722758","https://openalex.org/W2952267213","https://openalex.org/W2952813980","https://openalex.org/W2962854379","https://openalex.org/W2962985882","https://openalex.org/W2963167310","https://openalex.org/W2963167649","https://openalex.org/W2963206148","https://openalex.org/W2963341956","https://openalex.org/W2963748441","https://openalex.org/W2963825865","https://openalex.org/W2963963856","https://openalex.org/W2964309167","https://openalex.org/W2964352131","https://openalex.org/W2966404868","https://openalex.org/W2970648534","https://openalex.org/W2971277071","https://openalex.org/W3100949457","https://openalex.org/W3104777900","https://openalex.org/W3156017614","https://openalex.org/W3171266972","https://openalex.org/W3177155497","https://openalex.org/W4205807230","https://openalex.org/W4236521339","https://openalex.org/W4385245566","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2280422768","https://openalex.org/W3143197806","https://openalex.org/W4252555497","https://openalex.org/W3121175838","https://openalex.org/W3016293053","https://openalex.org/W2401723157","https://openalex.org/W2952904874","https://openalex.org/W324626582","https://openalex.org/W4389302559","https://openalex.org/W1690653314"],"abstract_inverted_index":{"Recently,":[0],"knowledge-grounded":[1,39,85],"dialogue":[2,20,34,55,86,109,156,166],"systems":[3],"have":[4,10],"gained":[5],"increasing":[6],"attention.":[7],"Great":[8],"efforts":[9],"been":[11],"made":[12],"to":[13,49,71],"build":[14],"response":[15,146,170],"matching":[16,45],"models":[17,68],"where":[18,91],"all":[19],"content":[21,35,167],"and":[22,30,47,57,95,107,130,138,141,162,165,173,179,188],"knowledge":[23,28,59,93,105,137,164],"sentences":[24,106],"are":[25,99],"leveraged.":[26],"However,":[27],"redundancy":[29],"distraction":[31],"of":[32,64,74,115,121,136,143],"irrelevant":[33,54,104],"often":[36],"exist":[37],"in":[38],"conversations,":[40],"which":[41],"may":[42],"affect":[43],"the":[44,62,72,113,119,134,155,174,198],"process":[46],"lead":[48],"inferior":[50],"performance.":[51],"In":[52],"addition,":[53],"history":[56],"excessive":[58],"also":[60],"hinder":[61],"exploitation":[63],"popular":[65],"pre-trained":[66],"language":[67],"(PLMs)":[69],"due":[70],"limitation":[73],"input":[75],"length.":[76],"To":[77],"address":[78],"these":[79],"challenges,":[80],"we":[81,124],"propose":[82],"a":[83,92,96],"new":[84],"model":[87,157,176,194],"based":[88],"on":[89,185],"PLMs,":[90],"selector":[94,98],"context":[97,139],"designed":[100],"for":[101,118,145,168],"filtering":[102],"out":[103],"redundant":[108],"history,":[110],"respectively.":[111],"Considering":[112],"lack":[114],"labeled":[116],"data":[117],"learning":[120,150],"two":[122,186],"selectors,":[123],"pre-train":[125],"them":[126],"with":[127,148],"weakly-supervised":[128],"tasks":[129],"then":[131],"jointly":[132],"conduct":[133,183],"optimization":[135],"selection":[140],"fine-tuning":[142],"PLMs":[144],"ranking":[147,171],"reinforcement":[149],"(RL).":[151],"By":[152],"this":[153],"means,":[154],"can":[158,177,195],"distill":[159],"more":[160],"accurate":[161],"concise":[163],"subsequent":[169],"module,":[172],"overall":[175],"converge":[178],"perform":[180],"better.":[181],"We":[182],"experiments":[184],"benchmarks":[187],"evaluation":[189],"results":[190],"indicate":[191],"that":[192],"our":[193],"significantly":[196],"outperform":[197],"state-of-the-art":[199],"methods.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
