{"id":"https://openalex.org/W3121096694","doi":"https://doi.org/10.1109/ialp51396.2020.9310512","title":"Detect Turn-takings in Subtitle Streams with Semantic Recall Transformer Encoder","display_name":"Detect Turn-takings in Subtitle Streams with Semantic Recall Transformer Encoder","publication_year":2020,"publication_date":"2020-12-04","ids":{"openalex":"https://openalex.org/W3121096694","doi":"https://doi.org/10.1109/ialp51396.2020.9310512","mag":"3121096694"},"language":"en","primary_location":{"id":"doi:10.1109/ialp51396.2020.9310512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp51396.2020.9310512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Asian Language Processing (IALP)","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/A5102376823","display_name":"Yuhai Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhai Liang","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101929507","display_name":"Qiang Zhou","orcid":"https://orcid.org/0000-0001-7369-3598"},"institutions":[{"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"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Zhou","raw_affiliation_strings":["Dept. of Computer Science and Technology, Institute of Artificial Intelligence, Beijing, China","Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Technology, Institute of Artificial Intelligence, Beijing, China","institution_ids":["https://openalex.org/I4210100255"]},{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102376823"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19042897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9995999932289124,"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":0.9994999766349792,"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":0.7648583650588989},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.7351773381233215},{"id":"https://openalex.org/keywords/subtitle","display_name":"Subtitle","score":0.7235739231109619},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6283947229385376},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5365042686462402},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5329068303108215},{"id":"https://openalex.org/keywords/turn-taking","display_name":"Turn-taking","score":0.5303307771682739},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5186328291893005},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5135383605957031},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5107321739196777},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4735029339790344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4627860188484192},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4627317190170288},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4594283103942871},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.45534026622772217},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4494740664958954},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.42472514510154724},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.4197652339935303},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.18232542276382446},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08426079154014587}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7648583650588989},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.7351773381233215},{"id":"https://openalex.org/C2780364048","wikidata":"https://www.wikidata.org/wiki/Q204028","display_name":"Subtitle","level":2,"score":0.7235739231109619},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6283947229385376},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5365042686462402},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5329068303108215},{"id":"https://openalex.org/C2776352735","wikidata":"https://www.wikidata.org/wiki/Q2313343","display_name":"Turn-taking","level":3,"score":0.5303307771682739},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5186328291893005},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5135383605957031},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5107321739196777},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4735029339790344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4627860188484192},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4627317190170288},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4594283103942871},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.45534026622772217},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4494740664958954},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.42472514510154724},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.4197652339935303},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.18232542276382446},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08426079154014587},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ialp51396.2020.9310512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp51396.2020.9310512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5699999928474426,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W194577561","https://openalex.org/W1566289585","https://openalex.org/W1591706642","https://openalex.org/W2064675550","https://openalex.org/W2141200610","https://openalex.org/W2157331557","https://openalex.org/W2157462866","https://openalex.org/W2166590182","https://openalex.org/W2250296630","https://openalex.org/W2397490041","https://openalex.org/W2498260772","https://openalex.org/W2509717730","https://openalex.org/W2537749207","https://openalex.org/W2567070169","https://openalex.org/W2604444020","https://openalex.org/W2792764867","https://openalex.org/W2806412155","https://openalex.org/W2885644005","https://openalex.org/W2896457183","https://openalex.org/W2950898568","https://openalex.org/W2951039930","https://openalex.org/W2962716111","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2963970792","https://openalex.org/W2980508402","https://openalex.org/W3004511899","https://openalex.org/W3011861456","https://openalex.org/W3102373121","https://openalex.org/W4212955756","https://openalex.org/W4385245566","https://openalex.org/W6635590879","https://openalex.org/W6683124362","https://openalex.org/W6690815549","https://openalex.org/W6691658881","https://openalex.org/W6712339791","https://openalex.org/W6731370813","https://openalex.org/W6739901393","https://openalex.org/W6749825310","https://openalex.org/W6752136759","https://openalex.org/W6755207826","https://openalex.org/W6769001469","https://openalex.org/W6776914297","https://openalex.org/W6831922518"],"related_works":["https://openalex.org/W1950334511","https://openalex.org/W2468279273","https://openalex.org/W2529301793","https://openalex.org/W2384121599","https://openalex.org/W3119551990","https://openalex.org/W2903456225","https://openalex.org/W1978877097","https://openalex.org/W3208962307","https://openalex.org/W2595453317","https://openalex.org/W2558461613"],"abstract_inverted_index":{"Subtitles":[0],"are":[1,54],"precious":[2],"dialogue":[3,61],"text":[4],"data":[5],"because":[6],"of":[7,15,153,156,163],"similarity":[8],"to":[9,39,92,111],"human":[10],"conversation,":[11],"but":[12],"the":[13,48,60,75,78,105,113,116,122,131,136,151,154,157,161,172,179],"lack":[14],"turn":[16],"structures":[17],"limits":[18],"their":[19],"applications":[20],"in":[21,32,178],"many":[22],"NLP":[23],"tasks.":[24],"The":[25,52],"previous":[26],"work":[27],"takes":[28],"turn-taking":[29,45,123],"detection":[30],"(TTD)":[31],"subtitles":[33],"as":[34,68],"a":[35,44,69,85],"sentence-pair":[36],"classification":[37],"problem":[38],"predict":[40],"if":[41],"there":[42],"is":[43],"happened":[46],"between":[47],"two":[49],"adjacent":[50],"utterances.":[51],"results":[53],"not":[55],"good":[56],"enough.":[57],"For":[58],"considering":[59],"context":[62,126],"information,":[63],"we":[64,83,103,149],"innovatively":[65],"take":[66],"TTD":[67],"sentence-level":[70],"sequence":[71],"labelling":[72],"problem,":[73],"predicting":[74],"turn-takings":[76,114],"through":[77],"whole":[79],"subtitle":[80,146,158],"stream.":[81],"First,":[82],"present":[84],"novel":[86],"fine-tuning":[87],"method":[88],"that":[89],"enables":[90],"BERT":[91],"encode":[93],"utterances":[94],"into":[95,128],"embedding":[96,118],"set":[97],"with":[98,135],"effective":[99],"turntaking":[100],"features.":[101],"Then,":[102],"propose":[104],"Semantic":[106],"Recall":[107],"Transformer":[108],"(SRT)":[109],"model":[110,168],"detect":[112],"among":[115],"utterance":[117],"set,":[119],"by":[120],"taking":[121],"features":[124],"and":[125,144,160],"information":[127],"account":[129],"at":[130],"same":[132],"time.":[133],"Compared":[134],"baselines,":[137],"it":[138],"achieves":[139],"state-of-the-art":[140],"on":[141,166],"both":[142],"English":[143],"Chinese":[145],"corpus.":[147],"Moreover,":[148],"explored":[150],"impacts":[152],"length":[155],"stream":[159],"number":[162],"conversation":[164],"participants":[165],"our":[167],"performance,":[169],"which":[170],"show":[171],"performance":[173],"can":[174],"be":[175],"further":[176],"improved":[177],"future.":[180]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
