{"id":"https://openalex.org/W2978613765","doi":"https://doi.org/10.1109/ijcnn.2019.8852155","title":"A Transformer-Based Variational Autoencoder for Sentence Generation","display_name":"A Transformer-Based Variational Autoencoder for Sentence Generation","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978613765","doi":"https://doi.org/10.1109/ijcnn.2019.8852155","mag":"2978613765"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5114860516","display_name":"Danyang Liu","orcid":"https://orcid.org/0000-0002-5078-6908"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danyang Liu","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085695760","display_name":"Gongshen Liu","orcid":"https://orcid.org/0000-0001-5194-1570"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gongshen Liu","raw_affiliation_strings":["School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114860516"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":4.2005,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.95371675,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9993000030517578,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.938307523727417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.703462541103363},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6176324486732483},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5588252544403076},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5415688753128052},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.479114294052124},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4786754250526428},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4643711447715759},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4407522678375244},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39269864559173584},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2638336420059204},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08517420291900635}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.938307523727417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703462541103363},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6176324486732483},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5588252544403076},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5415688753128052},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.479114294052124},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4786754250526428},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4643711447715759},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4407522678375244},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39269864559173584},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2638336420059204},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08517420291900635},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852155","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852155","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1632114991","https://openalex.org/W1836465849","https://openalex.org/W1959608418","https://openalex.org/W2025768430","https://openalex.org/W2099471712","https://openalex.org/W2133564696","https://openalex.org/W2173681125","https://openalex.org/W2259472270","https://openalex.org/W2396566817","https://openalex.org/W2399880602","https://openalex.org/W2586756136","https://openalex.org/W2785543907","https://openalex.org/W2792210438","https://openalex.org/W2949117887","https://openalex.org/W2952729433","https://openalex.org/W2962824887","https://openalex.org/W2962965405","https://openalex.org/W2963045354","https://openalex.org/W2963223306","https://openalex.org/W2963279312","https://openalex.org/W2963373786","https://openalex.org/W2963600562","https://openalex.org/W2963773425","https://openalex.org/W2963804993","https://openalex.org/W2964121744","https://openalex.org/W2964268978","https://openalex.org/W2964308564","https://openalex.org/W3022187094","https://openalex.org/W4293411878","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6636649193","https://openalex.org/W6638667902","https://openalex.org/W6640963894","https://openalex.org/W6679434410","https://openalex.org/W6685158001","https://openalex.org/W6685356407","https://openalex.org/W6688384872","https://openalex.org/W6692563993","https://openalex.org/W6712395597","https://openalex.org/W6718379498","https://openalex.org/W6727862155","https://openalex.org/W6731535438","https://openalex.org/W6734716764","https://openalex.org/W6739901393","https://openalex.org/W6745740328","https://openalex.org/W6748148878","https://openalex.org/W6749351710","https://openalex.org/W6763509872"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2988134182","https://openalex.org/W2770818364"],"abstract_inverted_index":{"The":[0],"variational":[1,27,43],"autoencoder(VAE)":[2],"has":[3],"been":[4,22],"proved":[5],"to":[6,39,72,84,109],"be":[7],"a":[8,58],"most":[9],"efficient":[10],"generative":[11],"model,":[12],"but":[13],"its":[14],"applications":[15],"in":[16,35,131],"natural":[17,30,46],"language":[18,69],"tasks":[19],"have":[20],"not":[21],"fully":[23,73],"developed.":[24],"A":[25],"novel":[26],"autoencoder":[28,44],"for":[29,45],"texts":[31],"generation":[32],"is":[33],"presented":[34],"this":[36],"paper.":[37],"Compared":[38],"the":[40,50,64,100,116,126,132],"previously":[41],"introduced":[42],"text":[47],"where":[48],"both":[49],"encoder":[51],"and":[52,62,96,106,125],"decoder":[53,65],"are":[54,122,128],"RNN-based,":[55],"we":[56,102],"propose":[57,81],"new":[59],"transformer-based":[60],"architecture":[61],"augment":[63],"with":[66,86],"an":[67],"LSTM":[68],"model":[70,97],"layer":[71],"exploit":[74],"information":[75],"of":[76],"latent":[77,133],"variables.":[78],"We":[79],"also":[80],"some":[82],"methods":[83],"deal":[85],"problems":[87],"during":[88],"training":[89],"time,":[90],"such":[91],"as":[92],"KL":[93],"divergency":[94],"collapsing":[95],"degradation.":[98],"In":[99],"experiment,":[101],"use":[103],"random":[104],"sampling":[105],"linear":[107],"interpolation":[108],"test":[110],"our":[111,120],"model.":[112],"Results":[113],"show":[114],"that":[115],"generated":[117],"sentences":[118],"by":[119],"approach":[121],"more":[123,129],"meaningful":[124],"semantics":[127],"coherent":[130],"space.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
