{"id":"https://openalex.org/W2799022285","doi":"https://doi.org/10.18653/v1/d18-1426","title":"Unsupervised Natural Language Generation with Denoising Autoencoders","display_name":"Unsupervised Natural Language Generation with Denoising Autoencoders","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2799022285","doi":"https://doi.org/10.18653/v1/d18-1426","mag":"2799022285"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1426","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1426","pdf_url":"https://www.aclweb.org/anthology/D18-1426.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1426.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068438716","display_name":"Markus Freitag","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Markus Freitag","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102104248","display_name":"Scott Roy","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Scott Roy","raw_affiliation_strings":["Google (United States), Mountain View, United States"],"affiliations":[{"raw_affiliation_string":"Google (United States), Mountain View, United States","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068438716"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":2.18418705,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88441331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3922","last_page":"3929"},"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":0.9998999834060669,"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":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8043515682220459},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6762552261352539},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6497313976287842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.647230327129364},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.6086049675941467},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5787467360496521},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5672035217285156},{"id":"https://openalex.org/keywords/dialog-box","display_name":"Dialog box","score":0.5666289329528809},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5601434111595154},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.529194712638855},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5259518623352051},{"id":"https://openalex.org/keywords/natural-language-generation","display_name":"Natural language generation","score":0.4903261959552765},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3672906756401062},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35059356689453125},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32376980781555176}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043515682220459},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6762552261352539},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6497313976287842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.647230327129364},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.6086049675941467},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5787467360496521},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5672035217285156},{"id":"https://openalex.org/C173853756","wikidata":"https://www.wikidata.org/wiki/Q86915","display_name":"Dialog box","level":2,"score":0.5666289329528809},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5601434111595154},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.529194712638855},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5259518623352051},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.4903261959552765},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3672906756401062},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35059356689453125},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32376980781555176},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","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},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/d18-1426","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1426","pdf_url":"https://www.aclweb.org/anthology/D18-1426.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1804.07899","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.07899","pdf_url":"https://arxiv.org/pdf/1804.07899","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2799022285","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1804.07899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1804.07899","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1804.07899","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1426","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1426","pdf_url":"https://www.aclweb.org/anthology/D18-1426.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2799022285.pdf","grobid_xml":"https://content.openalex.org/works/W2799022285.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1753482797","https://openalex.org/W2101105183","https://openalex.org/W2154652894","https://openalex.org/W2171421863","https://openalex.org/W2523790121","https://openalex.org/W2949888546","https://openalex.org/W2951176429","https://openalex.org/W2962784628","https://openalex.org/W2962905474","https://openalex.org/W2963804993","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2962768052","https://openalex.org/W2101105183","https://openalex.org/W3213792907","https://openalex.org/W2963341956","https://openalex.org/W3055227258","https://openalex.org/W2884607399","https://openalex.org/W2899474891","https://openalex.org/W3044881747","https://openalex.org/W3013160170","https://openalex.org/W3035118106","https://openalex.org/W2796074310","https://openalex.org/W3184735396","https://openalex.org/W592818541","https://openalex.org/W2966779809","https://openalex.org/W2935103971","https://openalex.org/W2246822104","https://openalex.org/W3136302691","https://openalex.org/W3135651738","https://openalex.org/W2151410774","https://openalex.org/W3175294391"],"abstract_inverted_index":{"Generating":[0],"text":[1],"from":[2],"structured":[3,57,89,104],"data":[4,58],"is":[5],"important":[6],"for":[7],"various":[8],"tasks":[9],"such":[10],"as":[11,59],"question":[12],"answering":[13],"and":[14,28,67,91],"dialog":[15],"systems.":[16],"We":[17,76],"show":[18,77],"that":[19,85,92],"in":[20],"at":[21],"least":[22],"one":[23],"domain,":[24],"without":[25],"any":[26],"supervision":[27],"only":[29],"based":[30],"on":[31],"unlabeled":[32],"text,":[33],"we":[34,54],"are":[35],"able":[36],"to":[37,72,79,98],"build":[38],"a":[39,60,69],"Natural":[40],"Language":[41],"Generation":[42],"(NLG)":[43],"system":[44],"with":[45],"higher":[46],"performance":[47],"than":[48],"supervised":[49],"approaches.":[50],"In":[51],"our":[52],"approach,":[53],"interpret":[55],"the":[56,64,74,93],"corrupt":[61],"representation":[62],"of":[63],"desired":[65],"output":[66],"use":[68],"denoising":[70,95],"auto-encoder":[71,96],"reconstruct":[73],"sentence.":[75],"how":[78],"introduce":[80],"noise":[81],"into":[82],"training":[83],"examples":[84],"do":[86],"not":[87],"contain":[88],"data,":[90],"resulting":[94],"generalizes":[97],"generate":[99],"correct":[100],"sentences":[101],"when":[102],"given":[103],"data.":[105]},"counts_by_year":[{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
