{"id":"https://openalex.org/W4291908513","doi":"https://doi.org/10.1109/icccs55155.2022.9846540","title":"Research on Multi-round Dialogue Tasks Based on Sequicity","display_name":"Research on Multi-round Dialogue Tasks Based on Sequicity","publication_year":2022,"publication_date":"2022-04-22","ids":{"openalex":"https://openalex.org/W4291908513","doi":"https://doi.org/10.1109/icccs55155.2022.9846540"},"language":"en","primary_location":{"id":"doi:10.1109/icccs55155.2022.9846540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs55155.2022.9846540","pdf_url":null,"source":{"id":"https://openalex.org/S4363608130","display_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10072/420590","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089657830","display_name":"Yingrui Pang","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingrui Pang","raw_affiliation_strings":["Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","School of Information Science &amp"],"affiliations":[{"raw_affiliation_string":"Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"School of Information Science &amp","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088502207","display_name":"Zhenni Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenni Gong","raw_affiliation_strings":["Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","School of Information Science &amp"],"affiliations":[{"raw_affiliation_string":"Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"School of Information Science &amp","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101737196","display_name":"Zixuan Zhao","orcid":"https://orcid.org/0009-0007-1624-1510"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixuan Zhao","raw_affiliation_strings":["Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","School of Information Science &amp"],"affiliations":[{"raw_affiliation_string":"Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"School of Information Science &amp","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067892451","display_name":"Yanyan Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Xu","raw_affiliation_strings":["Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","School of Information Science &amp"],"affiliations":[{"raw_affiliation_string":"Beijing Forestry University,School of Information Science &amp; Technology,Beijing,China","institution_ids":["https://openalex.org/I31683504"]},{"raw_affiliation_string":"School of Information Science &amp","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102845007","display_name":"Dengfeng Ke","orcid":"https://orcid.org/0000-0001-8459-0412"},"institutions":[{"id":"https://openalex.org/I115212828","display_name":"Beijing Language and Culture University","ror":"https://ror.org/03te2zs36","country_code":"CN","type":"education","lineage":["https://openalex.org/I115212828"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dengfeng Ke","raw_affiliation_strings":["Beijing Language and Culture University,School of Information Science,Beijing,China","School of Information Science, Beijing Language and Culture University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Language and Culture University,School of Information Science,Beijing,China","institution_ids":["https://openalex.org/I115212828"]},{"raw_affiliation_string":"School of Information Science, Beijing Language and Culture University, Beijing, China","institution_ids":["https://openalex.org/I115212828"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037325282","display_name":"Kaile Su","orcid":"https://orcid.org/0000-0001-6741-9699"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kaile Su","raw_affiliation_strings":["Griffith University,Institute for Integrated and Intelligent Systems,Nathan,Australia","Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Australia"],"affiliations":[{"raw_affiliation_string":"Griffith University,Institute for Integrated and Intelligent Systems,Nathan,Australia","institution_ids":["https://openalex.org/I11701301"]},{"raw_affiliation_string":"Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5089657830"],"corresponding_institution_ids":["https://openalex.org/I31683504"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07719242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"43","issue":null,"first_page":"57","last_page":"62"},"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":0.9987999796867371,"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":0.9980000257492065,"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":0.842094898223877},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.825359046459198},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.697554349899292},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6243257522583008},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6034923791885376},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5897509455680847},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5787055492401123},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.559083878993988},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.46455758810043335},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4571203887462616},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4560302793979645},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3897980749607086},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25276660919189453},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13408946990966797}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.842094898223877},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.825359046459198},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.697554349899292},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6243257522583008},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6034923791885376},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5897509455680847},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5787055492401123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.559083878993988},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.46455758810043335},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4571203887462616},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4560302793979645},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3897980749607086},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25276660919189453},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13408946990966797},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icccs55155.2022.9846540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs55155.2022.9846540","pdf_url":null,"source":{"id":"https://openalex.org/S4363608130","display_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/420590","is_oa":true,"landing_page_url":"http://hdl.handle.net/10072/420590","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":{"id":"pmh:oai:research-repository.griffith.edu.au:10072/420590","is_oa":true,"landing_page_url":"http://hdl.handle.net/10072/420590","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1969152782","https://openalex.org/W2157331557","https://openalex.org/W2188365844","https://openalex.org/W2798914047","https://openalex.org/W2951287343","https://openalex.org/W2962883855","https://openalex.org/W2963134326","https://openalex.org/W2963412005","https://openalex.org/W2963797754","https://openalex.org/W2964165364","https://openalex.org/W3022187094","https://openalex.org/W3034186467","https://openalex.org/W3034548376","https://openalex.org/W3035072597","https://openalex.org/W3100353332","https://openalex.org/W3119466383","https://openalex.org/W6623987585","https://openalex.org/W6631190155","https://openalex.org/W6683258052","https://openalex.org/W6687045409","https://openalex.org/W6738601716","https://openalex.org/W6898505805"],"related_works":["https://openalex.org/W2372020181","https://openalex.org/W2156531654","https://openalex.org/W1950940422","https://openalex.org/W1581723585","https://openalex.org/W4378714697","https://openalex.org/W2294330161","https://openalex.org/W4283822356","https://openalex.org/W2187606256","https://openalex.org/W2940472653","https://openalex.org/W2129146436"],"abstract_inverted_index":{"Multi-round":[0],"dialogue":[1,16],"is":[2,188,208],"one":[3],"of":[4,26,53,81,130],"the":[5,41,48,51,54,58,76,79,82,88,92,98,102,105,111,115,121,127,131,135,145,149,152,164,168,173,178,184,203],"most":[6],"practical":[7],"techniques":[8],"in":[9,28],"natural":[10],"language":[11],"processing.":[12],"The":[13,124],"current":[14,49,132],"multi-round":[15],"systems":[17],"generally":[18],"suffer":[19],"from":[20,97,120],"contextual":[21],"information":[22,139],"loss":[23],"and":[24,57,91,114,134,142,160,196,216],"lack":[25],"diversity":[27],"generated":[29],"answers.":[30],"Therefore,":[31],"we":[32],"propose":[33],"a":[34,68],"model":[35,166],"based":[36],"on":[37,182],"Sequicity.":[38],"We":[39],"use":[40],"gate":[42],"recurrent":[43],"unit":[44],"(GRU)":[45],"to":[46,71,144],"encode":[47],"question,":[50],"response":[52,146],"previous":[55],"sentence":[56],"semantic":[59,137],"slot":[60,138],"information.":[61,74],"Then,":[62],"encoding":[63,128],"results":[64,80,107,129,180],"are":[65,85,95,108,118,140],"fed":[66],"into":[67,87,110],"context":[69,73],"encoder":[70],"generate":[72],"During":[75,101],"training":[77],"procedure,":[78,104],"two":[83],"encoders":[84],"input":[86,109,143],"recognition":[89,99],"network,":[90,113],"latent":[93,116,125],"variables":[94,117],"sampled":[96,119],"network;":[100],"test":[103],"concatenating":[106],"prior":[112,122],"network.":[123],"variables,":[126],"question":[133],"above":[136],"concatenated":[141],"decoder.":[147],"Finally,":[148],"decoder":[150],"employs":[151],"Softmax":[153],"function":[154],"for":[155],"decoding.":[156],"On":[157,202],"both":[158],"CamRest":[159],"KVRET":[161,204],"public":[162],"datasets,":[163],"proposed":[165],"achieves":[167],"best":[169,179],"results.":[170],"Compared":[171],"with":[172],"baseline":[174],"Sequicity,":[175],"which":[176],"had":[177],"before":[181],"CamRest,":[183],"model's":[185],"Success":[186,206],"F1":[187,207],"relatively":[189,209],"improved":[190,210],"by":[191,194,200,211,214,220],"3.4%,":[192],"BLEU":[193,213],"9.6%":[195],"Entity":[197,217],"match":[198],"rate":[199],"3.3%.":[201],"dataset,":[205],"2.0%,":[212],"1.5%":[215],"Match":[218],"Rate":[219],"2.8%.":[221]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
