{"id":"https://openalex.org/W7169785022","doi":"https://doi.org/10.48550/arxiv.2607.15648","title":"On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels","display_name":"On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels","publication_year":2026,"publication_date":"2026-07-17","ids":{"openalex":"https://openalex.org/W7169785022","doi":"https://doi.org/10.48550/arxiv.2607.15648"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.15648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15648","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.15648","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141210906","display_name":"Taiga Mori","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mori, Taiga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141214673","display_name":"Koji Inoue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Inoue, Koji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141203220","display_name":"Divesh Lala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lala, Divesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141206406","display_name":"Tatsuya Kawahara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawahara, Tatsuya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.7207000255584717,"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":0.7207000255584717,"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/T10465","display_name":"Neurobiology of Language and Bilingualism","score":0.05620000138878822,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10383","display_name":"Language, Discourse, Communication Strategies","score":0.04450000077486038,"subfield":{"id":"https://openalex.org/subfields/1203","display_name":"Language and Linguistics"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6295999884605408},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6256999969482422},{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.5945000052452087},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4860000014305115},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.46470001339912415}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095000147819519},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6295999884605408},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6256999969482422},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.5945000052452087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5378999710083008},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5131999850273132},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4860000014305115},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.46470001339912415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3010999858379364},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2777999937534332},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.15648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15648","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.15648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.15648","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,60],"multi-party":[1,76],"dialogues":[2],"between":[3],"a":[4,18,29,34,71,75,104],"dialogue":[5,78],"system":[6],"and":[7,52,95,123,135],"multiple":[8,82],"users,":[9],"identifying":[10],"to":[11,114,131],"whom":[12],"an":[13,38],"utterance":[14],"is":[15,17,49],"addressed":[16],"key":[19],"challenge.":[20],"Prior":[21],"work":[22],"has":[23,53],"typically":[24],"treated":[25],"addressee":[26,93,170],"detection":[27,171],"as":[28,70,116,118],"multi-class":[30],"classification":[31],"task,":[32],"selecting":[33],"single":[35],"label":[36],"representing":[37],"individual":[39],"participant":[40],"or":[41],"the":[42,166,175],"group.":[43],"This":[44],"formulation":[45],"assumes":[46],"that":[47,157],"address":[48,69,88,97,145,158],"inherently":[50],"discrete":[51,154],"primarily":[54],"been":[55],"used":[56],"for":[57],"predicting":[58],"turn-taking.":[59],"this":[61,65,178],"paper,":[62],"we":[63,84,164],"revisit":[64],"assumption":[66],"by":[67,81],"analyzing":[68],"continuous":[72,96,144],"phenomenon.":[73],"Using":[74],"human":[77],"corpus":[79],"annotated":[80],"annotators,":[83],"construct":[85],"both":[86,133],"binary":[87],"labels":[89,94],"derived":[90],"from":[91,100],"majority-vote":[92],"levels":[98,146],"inferred":[99],"annotator":[101],"judgments":[102],"using":[103,143,153],"latent-variable":[105],"model.":[106],"We":[107],"then":[108],"examine":[109],"how":[110],"these":[111],"representations":[112],"relate":[113],"turn-taking":[115],"well":[117],"listener":[119],"behaviors,":[120],"including":[121],"gaze":[122,134],"backchannels.":[124],"Our":[125],"results":[126],"show":[127],"that,":[128],"in":[129],"addition":[130],"turn-taking,":[132],"backchannels":[136],"are":[137],"associated":[138],"with":[139],"address.":[140],"Furthermore,":[141],"models":[142],"achieve":[147],"better":[148],"predictive":[149],"fit":[150],"than":[151],"those":[152],"labels,":[155],"suggesting":[156],"may":[159],"exhibit":[160],"graded":[161],"structure.":[162],"Finally,":[163],"discuss":[165],"future":[167],"directions":[168],"of":[169,177],"research":[172],"based":[173],"on":[174],"findings":[176],"study.":[179]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-21T00:00:00"}