{"id":"https://openalex.org/W2889411261","doi":"https://doi.org/10.18653/v1/d18-1420","title":"QuaSE: Sequence Editing under Quantifiable Guidance","display_name":"QuaSE: Sequence Editing under Quantifiable Guidance","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2889411261","doi":"https://doi.org/10.18653/v1/d18-1420","mag":"2889411261"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1420","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1420","pdf_url":"https://www.aclweb.org/anthology/D18-1420.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":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1420.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103152813","display_name":"Yi Liao","orcid":"https://orcid.org/0000-0003-2470-2333"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Yi Liao","raw_affiliation_strings":["Noah's Ark Lab, Huawei Technologies"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei Technologies","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086674741","display_name":"Lidong Bing","orcid":"https://orcid.org/0000-0003-4565-6313"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lidong Bing","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061435467","display_name":"Piji Li","orcid":"https://orcid.org/0000-0003-1474-3692"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Piji Li","raw_affiliation_strings":["Noah's Ark Lab, Huawei Technologies"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei Technologies","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087920747","display_name":"Shuming Shi","orcid":"https://orcid.org/0000-0001-7018-0682"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuming Shi","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111426224","display_name":"Wai Lam","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wai Lam","raw_affiliation_strings":["The Chinese University of Hong Kong"],"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100378800","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0002-7025-6365"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Zhang","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5103152813"],"corresponding_institution_ids":["https://openalex.org/I4210159102"],"apc_list":null,"apc_paid":null,"fwci":3.9092,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.94839742,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3855","last_page":"3864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10028","display_name":"Topic Modeling","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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/outcome","display_name":"Outcome (game theory)","score":0.8314803242683411},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.7638567686080933},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7427011728286743},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286498546600342},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5847589373588562},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5622076988220215},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5614752173423767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4743543863296509},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.46175098419189453},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4414554834365845},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3647598624229431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2585974335670471},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.20521202683448792},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11496984958648682},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08029398322105408}],"concepts":[{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.8314803242683411},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.7638567686080933},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7427011728286743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286498546600342},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5847589373588562},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5622076988220215},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5614752173423767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4743543863296509},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46175098419189453},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4414554834365845},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3647598624229431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2585974335670471},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.20521202683448792},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11496984958648682},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08029398322105408},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1420","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1420","pdf_url":"https://www.aclweb.org/anthology/D18-1420.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"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1420","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1420","pdf_url":"https://www.aclweb.org/anthology/D18-1420.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":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320322942","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889411261.pdf","grobid_xml":"https://content.openalex.org/works/W2889411261.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2123442489","https://openalex.org/W2130942839","https://openalex.org/W2475287302","https://openalex.org/W2519284111","https://openalex.org/W2539809671","https://openalex.org/W2581637843","https://openalex.org/W2607768201","https://openalex.org/W2617566453","https://openalex.org/W2740094762","https://openalex.org/W2788321882","https://openalex.org/W2812757605","https://openalex.org/W2962753250","https://openalex.org/W2963018920","https://openalex.org/W2963223306","https://openalex.org/W2963241138","https://openalex.org/W2963366196","https://openalex.org/W2963631950","https://openalex.org/W2963667126","https://openalex.org/W2963784072","https://openalex.org/W2964008635","https://openalex.org/W2964091575","https://openalex.org/W2964222296","https://openalex.org/W3101380508","https://openalex.org/W4298386828"],"related_works":["https://openalex.org/W2978999882","https://openalex.org/W2153369162","https://openalex.org/W2181392282","https://openalex.org/W2119369480","https://openalex.org/W3141031773","https://openalex.org/W1595686156","https://openalex.org/W148937441","https://openalex.org/W2375873920","https://openalex.org/W1027719266","https://openalex.org/W2044769131"],"abstract_inverted_index":{"We":[0],"propose":[1],"the":[2,30,33,37,41,65,69,75,79,92,101,106,121,130,134,139,156,167,172,180,188,207,220],"task":[3],"of":[4,29,35,40,155,182,190,193,202,222],"Quantifiable":[5],"Sequence":[6],"Editing":[7],"(QuaSE):":[8],"editing":[9,127],"an":[10,15,46,73,162],"input":[11,42,47,122],"sequence":[12,17,48],"to":[13,90,99,124,128,150,164,218],"generate":[14],"output":[16,163,185],"that":[18],"satisfies":[19],"a":[20,26,51,62,152,200],"given":[21],"numerical":[22],"outcome":[23,66,76,114,131,148,169],"value":[24],"measuring":[25],"certain":[27],"property":[28],"sequence,":[31,53],"with":[32,206],"requirement":[34],"keeping":[36],"main":[38],"content":[39,117,145],"sequence.":[43],"For":[44,61,196],"example,":[45],"could":[49,67,77],"be":[50,68,78],"word":[52],"such":[54],"as":[55,209],"review":[56,63,70,204],"sentence":[57,123],"and":[58,95,116,132,147,170,216],"advertisement":[59],"text.":[60],"sentence,":[64],"rating;":[71],"for":[72],"advertisement,":[74],"click-through":[80],"rate.":[81],"The":[82,174],"major":[83],"challenge":[84],"in":[85],"performing":[86],"QuaSE":[87],"is":[88],"how":[89],"perceive":[91],"outcome-related":[93],"wordings,":[94],"only":[96],"edit":[97],"them":[98],"change":[100,129],"outcome.":[102,210],"In":[103],"this":[104],"paper,":[105],"proposed":[107],"framework":[108,137],"contains":[109],"two":[110],"latent":[111,157,191],"factors,":[112,158],"namely,":[113],"factor":[115],"factor,":[118],"disentangled":[119],"from":[120],"allow":[125],"convenient":[126],"keep":[133,171],"content.":[135,173],"Our":[136],"explores":[138],"pseudo-parallel":[140,194],"sentences":[141,205],"by":[142,186],"modeling":[143],"their":[144],"similarity":[146],"differences":[149],"enable":[151],"better":[153,165],"disentanglement":[154],"which":[159],"allows":[160],"generating":[161,183],"satisfy":[166],"desired":[168],"dual":[175],"reconstruction":[176],"structure":[177],"further":[178],"enhances":[179],"capability":[181],"expected":[184],"exploiting":[187],"couplings":[189],"factors":[192],"sentences.":[195],"evaluation,":[197],"we":[198],"prepared":[199],"dataset":[201],"Yelp":[203],"ratings":[208],"Extensive":[211],"experimental":[212],"results":[213],"are":[214],"reported":[215],"discussed":[217],"elaborate":[219],"peculiarities":[221],"our":[223],"framework.":[224],"1":[225]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}